Characterisation of two desiccation-linked dehydrins from Xerophyta humilis

In response to abiotic stresses, organisms throughout the plant kingdom, as well as microorganisms and micro-animals such as nematodes or tardigrades, have been observed to express Late Embryogenesis Abundant (LEA) proteins as protective mechanisms. However, despite two decades of research, little is understood about their physiological functions and this has led to extensive nomenclature, with a large amount of redundancy. The primary reason for this lack of insight into LEA protein functions is their highly hydrophilic and intrinsically disordered nature. Intrinsically disordered proteins (IDPs) cannot be studied using conventional methods of structural analyses such as X-ray crystallography and, therefore, alternative techniques are required. A combination of transgenic and in vitro studies have also shown that LEA proteins are most likely to behave as molecular chaperones by binding water and ions, preventing macromolecular aggregation and protecting enzymatic activity during dehydration. This study characterized two dehydrins that were expressed during dehydration in the desiccation tolerant plant, Xerophyta humilis. From a transcriptome analyses on X. humilis, cDNA for the two dehydrins were obtained. These sequences were first analysed using various in silico tools in order to identify putative dehydrin-specific characteristics. Subsequently, these two dehydrins were cloned and expressed for production of recombinant dehydrin protein. These proteins were then analysed in terms of structural and functional characteristics. Structurally, through the use of circular dichroism in an in vitro system, both dehydrins demonstrated the shift towards being increasingly alpha-helical when placed in environments of decreasing water content. The role of these two dehydrins in stabilizing enzymes during dehydration was subsequently investigated using citrate synthase (CS) and lactate dehydrogenase (LDH). The preservation of enzyme activity was observed in both CS and LDH. This preservation of enzyme activity was further maintained by the presence of trehalose. Anti-aggregation roles were also investigated, however, neither dehydrin demonstrated significant ability to minimize the aggregation of LDH. This study hopes to establish a pipeline for characterizing LEA proteins using structural and functional assays in order to provide alternative means of LEA protein classification

Econometric Analysis of Production Networks with Dominant Units

This paper builds on the work of Acemoglu et al. (2012) and considers a production network with unobserved common technological factor and establishes general conditions under which the network structure contributes to aggregate fluctuations. It introduces the notions of strongly and weakly dominant units, and shows that at most a finite number of units in the network can be strongly dominant, while the number of weakly dominant units can rise with N (the cross section dimension). This paper further establishes the equivalence between the highest degree of dominance in a network and the inverse of the shape parameter of the power law. A new extremum estimator for the degree of pervasiveness of individual units in the network is proposed, and is shown to be robust to the choice of the underlying distribution. Using Monte Carlo techniques, the proposed estimator is shown to have satisfactory small sample properties. Empirical applications to US input-output tables suggest the presence of production sectors with a high degree of pervasiveness, but their effects are not sufficiently pervasive to be considered as strongly dominant

Structural Econometric Estimation of the Basic Reproduction Number for Covid-19 Across U.S. States and Selected Countries

This paper proposes a structural econometric approach to estimating the basic reproduction number ($\mathcal{R}_{0}$) of Covid-19. This approach identifies $\mathcal{R}_{0}$ in a panel regression model by filtering out the effects of mitigating factors on disease diffusion and is easy to implement. We apply the method to data from 48 contiguous U.S. states and a diverse set of countries. Our results reveal a notable concentration of $\mathcal{R}_{0}$ estimates with an average value of 4.5. Through a counterfactual analysis, we highlight a significant underestimation of the $\mathcal{R}_{0}$ when mitigating factors are not appropriately accounted for

Common Factors and Spatial Dependence: An Application to US House Prices

This paper considers panel data models with cross-sectional dependence arising from both spatial autocorrelation and unobserved common factors. It derives conditions for model identification and proposes estimation methods that employ cross-sectional averages as factor proxies, including the 2SLS, Best 2SLS, and GMM estimations. The proposed estimators are robust to unknown heteroskedasticity and serial correlation in the disturbances, unrequired to estimate the number of unknown factors, and computationally tractable. The paper establishes the asymptotic distributions of these estimators and compares their consistency and efficiency properties. Extensive Monte Carlo experiments lend support to the theoretical findings and demonstrate the satisfactory finite sample performance of the proposed estimators. The empirical section of the paper finds strong evidence of spatial dependence of real house price changes across 377 Metropolitan Statistical Areas in the US from 1975Q1 to 2014Q4. The results also reveal that population and income growth have significantly positive direct and spillover effects on house price changes. These findings are robust to different specifications of the spatial weights matrix constructed based on distance, migration flows, and pairwise correlations

Common Factors and Spatial Dependence: An Application to US House Prices

This paper considers panel data models with cross-sectional dependence arising from both spatial autocorrelation and unobserved common factors. It derives conditions for model identification and proposes estimation methods that employ cross-sectional averages as factor proxies, including the 2SLS, Best 2SLS, and GMM estimations. The proposed estimators are robust to unknown heteroskedasticity and serial correlation in the disturbances, unrequired to estimate the number of unknown factors, and computationally tractable. The paper establishes the asymptotic distributions of these estimators and compares their consistency and efficiency properties. Extensive Monte Carlo experiments lend support to the theoretical findings and demonstrate the satisfactory finite sample performance of the proposed estimators. The empirical section of the paper finds strong evidence of spatial dependence of real house price changes across 377 Metropolitan Statistical Areas in the US from 1975Q1 to 2014Q4. The results also reveal that population and income growth have significantly positive direct and spillover effects on house price changes. These findings are robust to different specifications of the spatial weights matrix constructed based on distance, migration flows, and pairwise correlations

The Impact of Rental Housing on Neighborhood Racial and Social Integration

Neighborhood racial and class segregation continue to be major social problems within America’s metropolitan areas. Segregation has been linked to a whole host of racial and class inequalities, including access to jobs, schooling and single parenthood, and future earnings. One factor accounting for segregation is the inability of black and lower income households to afford housing in white neighborhoods, where housing units historically have been largely owner-occupied single-family homes. In recent years there has been a shift in the housing makeup of many of these neighborhoods, with rentals and foreclosures increasing in share. This has made housing more affordable in these neighborhoods. In this paper we investigate the impact that foreclosures and three types of rentals (single-family, condominium, and apartments) have on neighborhood racial and income integration using data from Miami-Dade County, Florida. We find that neighborhoods have become more racially and socially integrated as rentals have increased as a share of the housing stock

The Impact of Rental Housing on Neighborhood Racial and Social Integration

Neighborhood racial and class segregation continue to be major social problems within America’s metropolitan areas. Segregation has been linked to a whole host of racial and class inequalities, including access to jobs, schooling and single parenthood, and future earnings. One factor accounting for segregation is the inability of black and lower income households to afford housing in white neighborhoods, where housing units historically have been largely owner-occupied single-family homes. In recent years there has been a shift in the housing makeup of many of these neighborhoods, with rentals and foreclosures increasing in share. This has made housing more affordable in these neighborhoods. In this paper we investigate the impact that foreclosures and three types of rentals (single-family, condominium, and apartments) have on neighborhood racial and income integration using data from Miami-Dade County, Florida. We find that neighborhoods have become more racially and socially integrated as rentals have increased as a share of the housing stock

The Small 11-kDa Protein from B19 Parvovirus Binds Growth Factor Receptor-Binding Protein 2 in Vitro in a Src Homology 3 Domain/Ligand-Dependent Manner

AbstractThe small 11-kDa proteins of B19 parvovirus contain three proline-rich regions which conform to consensus Src homology 3 (SH3) ligand sequences present in signaling molecules within the cell. We have shown that the B19 11-kDa proteins specifically interact with the growth factor receptor-binding protein 2 (Grb2) in vitro. Mutation of prolines within one of the three SH3 ligand-like sequences decreases the binding of B19 11-kDa proteins to Grb2, suggesting that the proline-rich region is involved in the B19 11-kDa/Grb2 interaction. Therefore, the B19 11-kDa proteins may function to alter Grb2-mediated signaling by disrupting SH3 domain/ligand interactions. These results implicate the 11-kDa proteins in B19 pathogenesis through perturbation of normal cellular signaling pathways

Lack of Association of Polymorphisms in Homocysteine Metabolism Genes with Pseudoexfoliation Syndrome and Glaucoma

Purpose: To evaluate genes involved in homocysteine metabolism as secondary risk factors for pseudoexfoliation syndrome (PXFS) and the associated glaucoma (PXFG). Methods: One hundred eighty-six unrelated patients with PXFS, including 140 patients with PXFG and 127 unrelated control subjects were recruited from the Massachusetts Eye and Ear Infirmary. All the patients and controls were Caucasian of European ancestry. Seventeen tag SNPs from 5 genes (methylenetetrahydrofolate reductase [MTHFR], methionine synthase [MTR], methionine synthase reductase [MTRR], methylenetetrahydrofolate dehydrogenase [MTHFD1], and cystathionine β-synthase [CBS]) were genotyped. Single-SNP association was analyzed using Fisher’s exact test (unconditional) or logistic regression after conditioning on the effects of age and three LOXL1 SNPs (rs1048661, rs3825942, and rs2165241). Interaction analysis was performed between the homocysteine and LOXL1 SNPs using logistic regression. Haplotype analysis and the set-based test were used to test for association of individual genes. Multiple comparisons were corrected using the Bonferroni method. Results: One SNP (rs8006686) in MTHFD1 showed a nominally significant association with PXFG (p=0.015, OR=2.23). None of the seventeen SNPs tested were significantly associated with PXFS or PXFG after correcting for multiple comparisons (Bonferroni corrected p>0.25). After controlling for the effects of age and three associated LOXL1 SNPs, none of the seventeen tested SNPs were associated with PXFS (p>0.12). No significant interaction effects on PXFS were identified between the homocysteine and LOXL1 SNPs (p>0.06). Haplotype analysis and the set-based test did not find significant association of individual genes with PXFS (p>0.23 and 0.20, respectively). Conclusions: Five genes that are critical components of the homocysteine metabolism pathway were evaluated as secondary factors for PXFS and PXFG. Our results suggest that these genes are not significant risk factors for the development of these conditions