Role of fire severity in controlling patterns of stand dominance following wildfire in boreal forests

Thesis (Ph.D.) University of Alaska Fairbanks, 2016Global trends of climate warming have been particularly pronounced in northern latitudes, and have been linked to an intensification of the fire regime in Arctic and boreal ecosystems. Increases in fire frequency, extent, and severity that have been observed over the past several decades are expected to continue under a warming climate. Severe fires can drastically reduce or remove the deep organic layers that accumulate in mature black spruce forests. Extensive studies in the boreal forests of interior Alaska and Canada have shown that parts of the landscape that undergo severe burning provide favorable seedbeds for the recruitment of deciduous tree seedlings, and thereby reduce the relative abundance of coniferous seedling recruitment in these areas shortly after fire. The persistence of deciduous species such as aspen beyond the seedling recruitment and establishment stage is as yet relatively unknown. To address this knowledge gap, I asked the question: is increased deciduous recruitment observed in severely burned areas transient, or does it result in persistent changes in stand composition later in succession? I examined changes in relative dominance patterns of aspen and black spruce that had occurred between 8 and 14 years post-fire along an organic layer depth gradient within a single burn. I found that patterns of relative species dominance established shortly after fire persisted into the second decade of succession, resulting in productive aspendominated stands in severely burned areas with shallow organic layers, and black spruce dominated stands in lightly burned areas with deep organic layers. These patterns of stand dominance in relation to post-fire organic layer depth were also observed in several other burns in the region. Therefore, deep burning fires are likely to result in a persistent shift from black spruce to aspen dominance in severely burned parts of the boreal forest. In order to understand how variation in organic layer depth is driving these alternate successional pathways, I measured nutrient uptake rates of aspen and spruce in severely and lightly burned sites within a single burn. I also examined relationships between post-fire organic layer depth and a suite of soil variables, and evaluated the relative importance of these soil variables in explaining variation in stand level aspen biomass, spruce biomass, and the relative dominance of aspen vs. spruce. I found that variations in post-fire organic layer depth result in contrasting soil environments, with soils in shallow organic layer sites being warmer, drier, and more alkaline than soils in deep organic layer sites. Variations in aspen biomass and aspen: spruce biomass were largely being driven by substrate conditions, whereas stand level spruce biomass was less sensitive to these same variations in soil conditions. Nutrient uptake rates of both aspen and spruce were higher in severely burned areas with shallow organic layers, but the differences between species were magnified by stand biomass patterns in relation to post-fire organic layer depth. My results suggest that the positive effects of soil conditions associated with mineral soil substrates extend well beyond the initial seedling recruitment phase, and may continue to influence aspen growth rates into the second decade of succession resulting in the differential patterns of biomass accumulation and stand dominance in relation to post-fire organic layer depth. With the predicted increase in fire severity and shortening of the fire cycle, the proportion of aspen dominated stands on the landscape is likely to increase, which will incur substantial changes in ecosystem function (e.g., land-atmosphere energy exchange, C and N storage, nutrient cycling, net primary productivity, and wildlife habitat quality) compared to the current forests dominated by conifers

Evaluation of AlphaFold-Multimer prediction on multi-chain protein complexes

Motivation Despite near-experimental accuracy on single-chain predictions, there is still scope for improvement among multimeric predictions. Methods like AlphaFold-Multimer and FoldDock can accurately model dimers. However, how well these methods fare on larger complexes is still unclear. Further, evaluation methods of the quality of multimeric complexes are not well established. Results We analysed the performance of AlphaFold-Multimer on a homology-reduced dataset of homo- and heteromeric protein complexes. We highlight the differences between the pairwise and multi-interface evaluation of chains within a multimer. We describe why certain complexes perform well on one metric (e.g. TM-score) but poorly on another (e.g. DockQ). We propose a new score, Predicted DockQ version 2 (pDockQ2), to estimate the quality of each interface in a multimer. Finally, we modelled protein complexes (from CORUM) and identified two highly confident structures that do not have sequence homology to any existing structures. Availability and implementation All scripts, models, and data used to perform the analysis in this study are freely available at https://gitlab.com/ElofssonLab/afm-benchmark

Predicting the structure of large protein complexes using AlphaFold and Monte Carlo tree search

AlphaFold can predict the structure of single- and multiple-chain proteins with very high accuracy. However, the accuracy decreases with the number of chains, and the available GPU memory limits the size of protein complexes which can be predicted. Here we show that one can predict the structure of large complexes starting from predictions of subcomponents. We assemble 91 out of 175 complexes with 10–30 chains from predicted subcomponents using Monte Carlo tree search, with a median TM-score of 0.51. There are 30 highly accurate complexes (TM-score ≥0.8, 33% of complete assemblies). We create a scoring function, mpDockQ, that can distinguish if assemblies are complete and predict their accuracy. We find that complexes containing symmetry are accurately assembled, while asymmetrical complexes remain challenging. The method is freely available and accesible as a Colab notebook https://colab.research.google.com/github/patrickbryant1/MoLPC/blob/master/MoLPC.ipynb

Ultrasound-enhanced ocular delivery of dexamethasone sodium phosphate: An in vivo study

Background The eye\u27s unique anatomy and its physiological and anatomical barriers can limit effective drug delivery into the eye. Methods An in vivo study was designed to determine the effectiveness and safety of ultrasound application in enhancing drug delivery in a rabbit model. Permeability of a steroid ophthalmic drug, dexamethasone sodium phosphate, was investigated in ultrasound- and sham-treated cases. For this study, an eye cup filled with dexamethasone sodium phosphate was placed on the cornea. Ultrasound was applied at intensity of 0.8 W/cm2 and frequency of 400 or 600 kHz for 5 min. The drug concentration in aqueous humor samples, collected 90 min after the treatment, was determined using chromatography methods. Light microscopy observations were done to determine the structural changes in the cornea as a result of ultrasound application. Results An increase in drug concentration in aqueous humor samples of 2.8 times (p \u3c 0.05) with ultrasound application at 400 kHz and 2.4 times (p \u3c 0.01) with ultrasound application at 600 kHz was observed as compared to sham-treated samples. Histological analysis showed that the structural changes in the corneas exposed to ultrasound predominantly consisted of minor epithelial disorganization. Conclusions Ultrasound application enhanced the delivery of an anti-inflammatory ocular drug, dexamethasone sodium phosphate, through the cornea in vivo. Ultrasound-enhanced ocular drug delivery appears to be a promising area of research with a potential future application in a clinical setting

Impact of personality traits on investment decision-making: Mediating role of investor sentiment in India

The behavior of investors and their investment decision-making process in the financial markets are guided by psychological (sentiments) and personal characteristics (personality traits). Research in recent years has shown the connection between investor sentiment and personality traits and investment decisions. Though academic works in the field of behavioral finance are growing, studies on personality traits and investment decision-making with investor sentiment as a mediator are sparse. To this end, the paper aims to analyze the effects of Indian retail investors’ Big-five personality traits (Neuroticism, Extraversion, Openness to experience, Agreeableness, and Conscientiousness) on their short-term and long-term investment decision-making with the mediating effect of investor sentiment. The study employs the Partial Least Square-Structural Equation Model to test the framed hypotheses. The findings of the study reveal that Neuroticism has a significant positive effect (β=0.352, p<0.05) on investor sentiment. It further shows that Extraversion has a significant positive effect (β=0.186, p<0.05) on long-term decision-making. On the contrary, the consciousness trait has a significant negative effect (β=-0.335, p<0.05) on short-term investment decision-making. Furthermore, the Openness trait demonstrates a significant effect on both short-term and long-term investment decision-making (β=0.357, p<0.05; β=0.007, p<0.05). However, the findings reveal no significant intervening effect of investor sentiment between personality traits and investment decision-making. Thus, the study strongly exerted the impact of investors’ personality traits on their investment decision-making due to the high influence of personal characteristics over sentiment effects

Do bond attributes affect green bond yield? Evidence from Indian green bonds

Over the years, green finance tools have gained considerable attention with the increased concern to achieve sustainability in the economy. Green bonds are one such new innovative green finance tool embodied with bonds and green attributes. However, research on the Indian green bond is relatively modest. Thus, this study aims to analyze the impact of bond attributes on green bond yield. The study retrieves green bond data from the Bloomberg and Climate Bonds Initiative databases from 2015 to 2022. To test the framed hypotheses, the study employs a panel regression technique with a random effect model. The findings of the study show a significant positive effect of bond ratings (β = 2.80926, p < 0.05) on green bond yield based on the argument that good-rated bonds serve as collateral in the security market. On the contrary, the result also reveals a significant negative effect of bond maturity (β = –0.327296, p < 0.05) and bond label (β = –3.16480, p < 0.05) on green bond yield. The results based on the observation suggest that when the certified bond is issued, this signals the greenness of the bond in the market and attracts high demand, whereas the long maturity ensures the green project construction for a longer period, resulting in a lower bond value. Thus, empirical findings reveal that bond attributes are the major factors in influencing bond yield. The obtained results serve as a prerequisite for potential issuers, investors, and policymakers to further popularize the green bond in the country

A structural biology community assessment of AlphaFold2 applications

Most proteins fold into 3D structures that determine how they function and orchestrate the biological processes of the cell. Recent developments in computational methods for protein structure predictions have reached the accuracy of experimentally determined models. Although this has been independently verified, the implementation of these methods across structural-biology applications remains to be tested. Here, we evaluate the use of AlphaFold2 (AF2) predictions in the study of characteristic structural elements; the impact of missense variants; function and ligand binding site predictions; modeling of interactions; and modeling of experimental structural data. For 11 proteomes, an average of 25% additional residues can be confidently modeled when compared with homology modeling, identifying structural features rarely seen in the Protein Data Bank. AF2-based predictions of protein disorder and complexes surpass dedicated tools, and AF2 models can be used across diverse applications equally well compared with experimentally determined structures, when the confidence metrics are critically considered. In summary, we find that these advances are likely to have a transformative impact in structural biology and broader life-science research

Identification and characterisation of a rare MTTP variant underlying hereditary non-alcoholic fatty liver disease

Background and aimsNon-alcoholic fatty liver disease (NAFLD) is a complex trait with an estimated prevalence of 25% globally. We aimed to identify the genetic variant underlying a four-generation family with progressive NAFLD leading to cirrhosis, decompensation and development of hepatocellular carcinoma in the absence of common risk factors such as obesity and type 2 diabetes.MethodsExome sequencing and genome comparisons were used to identify the likely causal variant. We extensively characterised the clinical phenotype and post-prandial metabolic responses of family members with the identified novel variant in comparison to healthy non-carriers and wild-type patients with NAFLD. Variant-expressing hepatocyte-like cells (HLCs) were derived from human induced pluripotent stem cells generated from homozygous donor skin fibroblasts and restored to wild-type using CRISPR-Cas9. The phenotype was assessed using imaging, targeted RNA analysis and molecular expression arrays.ResultsWe identified a rare causal variant c.1691T>C p.I564T (rs745447480) in MTTP, encoding microsomal triglyceride transfer protein (MTP), associated with progressive NAFLD, unrelated to metabolic syndrome and without characteristic features of abetalipoproteinemia. HLCs derived from a homozygote donor had significantly lower MTP activity and lower lipoprotein ApoB secretion compared to wild-type cells, while having similar levels of MTP mRNA and protein. Cytoplasmic triglyceride accumulation in HLCs triggered endoplasmic reticulum stress, secretion of pro-inflammatory mediators and production of reactive oxygen species.ConclusionWe have identified and characterized a rare causal variant in MTTP and homozygosity for MTTP p.I564T is associated with progressive NAFLD without any other manifestations of abetalipoproteinemia. Our findings provide insights into mechanisms driving progressive NAFLD

Common Genetic Polymorphisms Influence Blood Biomarker Measurements in COPD

Implementing precision medicine for complex diseases such as chronic obstructive lung disease (COPD) will require extensive use of biomarkers and an in-depth understanding of how genetic, epigenetic, and environmental variations contribute to phenotypic diversity and disease progression. A meta-analysis from two large cohorts of current and former smokers with and without COPD [SPIROMICS (N = 750); COPDGene (N = 590)] was used to identify single nucleotide polymorphisms (SNPs) associated with measurement of 88 blood proteins (protein quantitative trait loci; pQTLs). PQTLs consistently replicated between the two cohorts. Features of pQTLs were compared to previously reported expression QTLs (eQTLs). Inference of causal relations of pQTL genotypes, biomarker measurements, and four clinical COPD phenotypes (airflow obstruction, emphysema, exacerbation history, and chronic bronchitis) were explored using conditional independence tests. We identified 527 highly significant (p 10% of measured variation in 13 protein biomarkers, with a single SNP (rs7041; p = 10−392) explaining 71%-75% of the measured variation in vitamin D binding protein (gene = GC). Some of these pQTLs [e.g., pQTLs for VDBP, sRAGE (gene = AGER), surfactant protein D (gene = SFTPD), and TNFRSF10C] have been previously associated with COPD phenotypes. Most pQTLs were local (cis), but distant (trans) pQTL SNPs in the ABO blood group locus were the top pQTL SNPs for five proteins. The inclusion of pQTL SNPs improved the clinical predictive value for the established association of sRAGE and emphysema, and the explanation of variance (R2) for emphysema improved from 0.3 to 0.4 when the pQTL SNP was included in the model along with clinical covariates. Causal modeling provided insight into specific pQTL-disease relationships for airflow obstruction and emphysema. In conclusion, given the frequency of highly significant local pQTLs, the large amount of variance potentially explained by pQTL, and the differences observed between pQTLs and eQTLs SNPs, we recommend that protein biomarker-disease association studies take into account the potential effect of common local SNPs and that pQTLs be integrated along with eQTLs to uncover disease mechanisms. Large-scale blood biomarker studies would also benefit from close attention to the ABO blood group