Microstructural Assessment of a Multiple-Intermetallic-Strengthened Aluminum Alloy Produced from Gas-Atomized Powder by Hot Extrusion and Friction Extrusion.

An aluminum (Al) matrix with various transition metal (TM) additions is an effective alloying approach for developing high-specific-strength materials for use at elevated temperatures. Conventional fabrication processes such as casting or fusion-related methods are not capable of producing Al-TM alloys in bulk form. Solid phase processing techniques, such as extrusion, have been shown to maintain the microstructure of Al-TM alloys. In this study, extrusions are fabricated from gas-atomized aluminum powders (≈100-400 µm) that contain 12.4 wt % TM additives and an Al-based matrix reinforced by various Al-Fe-Cr-Ti intermetallic compounds (IMCs). Two different extrusion techniques, conventional hot extrusion and friction extrusion, are compared using fabricating rods. During extrusion, the strengthening IMC phases were extensively refined as a result of severe plastic deformation. Furthermore, the quasicrystal approximant IMC phase (70.4 wt % Al, 20.4 wt % Fe, 8.7 wt % Cr, 0.6 wt % Ti) observed in the powder precursor is replaced by new IMC phases such as Al3.2Fe and Al45Cr7-type IMCs. The Al3Ti-type IMC phase is partially dissolved into the Al matrix during extrusion. The combination of linear and rotational shear in the friction extrusion process caused severe deformation in the powders, which allowed for a higher extrusion ratio, eliminated linear voids, and resulted in higher ductility while maintaining strength comparable to that resulting from hot extrusion. Results from equilibrium thermodynamic calculations show that the strengthening IMC phases are stable at elevated temperatures (up to ≈ 600 °C), thus enhancing the high-temperature strength of the extrudates.

Liver Cancer Incidence and Area-Level Geographic Disparities in Pennsylvania-A Geo-Additive Approach.

Many neighborhood socioeconomic index measures (nSES) that capture neighborhood deprivation exist but the impact of measure selection on liver cancer (LC) geographic disparities remains unclear. We introduce a Bayesian geoadditive modeling approach to identify clusters in Pennsylvania (PA) with higher than expected LC incidence rates, adjusted for individual-level factors (age, sex, race, diagnosis year) and compared them to models with 7 different nSES index measures to elucidate the impact of nSES and measure selection on LC geospatial variation. LC cases diagnosed from 2007-2014 were obtained from the PA Cancer Registry and linked to nSES measures from U.S. census at the Census Tract (CT) level. Relative Risks (RR) were estimated for each CT, adjusted for individual-level factors (baseline model). Each nSES measure was added to the baseline model and changes in model fit, geographic disparity and state-wide RR ranges were compared. All 7 nSES measures were strongly associated with high risk clusters. Tract-level RR ranges and geographic disparity from the baseline model were attenuated after adjustment for nSES measures. Depending on the nSES measure selected, up to 60% of the LC burden could be explained, suggesting methodologic evaluations of multiple nSES measures may be warranted in future studies to inform LC prevention efforts.

Assessing the spatial heterogeneity in black-white differences in optimal cardiovascular health and the impact of individual- and neighborhood-level risk factors: The Multi-Ethnic Study of Atherosclerosis (MESA).

Racial disparities in cardiovascular health (CVH) continue to remain a public health concern in the United States. We use unique population-based data from the Multi-Ethnic Study of Atherosclerosis cohort to explore the black-white differences in optimal CVH. Utilizing geographically weighted regression methods, we assess the spatial heterogeneity in black-white differences in optimal CVH and the impact of both individual- and neighborhood-level risk factors. We found evidence of significant spatial heterogeneity in black-white differences that varied within and between the five sites. Initial models showed decreased odds of optimal CVH for blacks that ranged from 60% to 70% reduced odds - with noticeable variation of these decreased odds within each site. Adjusting for risk factors resulted in reductions in the black-white differences in optimal CVH. Further understanding of the reasons for spatial heterogeneities in black-white differences in nationally representative cohorts may provide important clues regarding the drivers of these differences.

Exploring the Spatial Patterning in Racial Differences in Cardiovascular Health Between Blacks and Whites Across the United States: The REGARDS Study.

Background Cardiovascular health (CVH) disparities between blacks and whites have persisted in the United States for some time, and although there have been remarkable improvements in addressing cardiovascular disease, it still remains the leading cause of death in the United States. In addition, well-documented disparities are unfortunately widening incidence gaps across certain regions of the United States. Our focus was on answering the following questions: (1) How much spatial heterogeneity exists in the racial differences in CVH between blacks and whites across this country? and (2) Is the spatial heterogeneity in the racial differences significantly explained by living in the Stroke Belt? Methods and Results To explore the spatial patterning in the racial differences in CVH between blacks and whites across the country, we used geographically weighted regression methods, which result in local estimates of the racial differences in CVH. Using data from the REGARDS (Reasons for Geographic and Racial Differences in Stroke) Study, we found significant spatial patterning in these racial differences, even beyond the well-known Stroke Belt and Stroke Buckle. All of the estimated differences indicated blacks consistently having diminishing CVH compared with whites, where this difference was largely noted in pockets of the Stroke Belt and Stroke Buckle, in addition to moderate to large disparities noted in the Great Lakes region, portions of the Northeast, and along the West coast. Conclusions Efforts to improve CVH and ultimately reduce disparities between blacks and whites require culturally competent methods, with a strong focus on geography-based interventions and policies.

Mortality in Patients With Chronic Renal Disease Without Health Insurance in Mexico: Opportunities for a National Renal Health Policy.

INTRODUCTION: Despite a systematic increase in the coverage of patients with end-stage renal disease (ESRD) who have received dialytic therapies and transplantation over the past 2 decades, the Mexican health system currently still does not have a program to provide full coverage of ESRD. Our aim was to analyze mortality in patients with ESRD without health insurance. METHODS: This was a prospective cohort study of 850 patients with advanced chronic kidney disease (CKD). Risk factors associated with death were calculated using a Cox's proportional hazards model. We used the statistical package SPSS version 22.0 for data analysis. RESULTS: The mean age of patients was 44.8 ± 17.2 years old. At the time of hospital admission, 87.6% of the population did not have a social security program to cover the cost of renal replacement treatment, and 91.3% of families had an income below US$300 per month. During the 3 years of the study, 28.8% of the cohort patients were enrolled in 1 of Mexico's social security programs. The 3-year mortality rate was of 56.7% among patients without access to health insurance, in contrast to 38.2% of patients who had access to a social security program that provided access to renal replacement therapy (P < 0.001). Risk factor analysis revealed that not having health insurance increased mortality (risk ratio: 2.64, 95% confidence intervals: 1.84-3.79; P = 0.001). CONCLUSION: Mexico needs a coordinated National Kidney Health and Treatment Program. A program of this nature should provide the basis for an appropriate educational and intervention strategy for early detection, prevention, and treatment of patients with advanced chronic kidney disease.

Microarray-based identification of antigenic variants of foot-and-mouth disease virus: a bioinformatics quality assessment.

BACKGROUND: The evolution of viral quasispecies can influence viral pathogenesis and the response to antiviral treatments. Mutant clouds in infected organisms represent the first stage in the genetic and antigenic diversification of RNA viruses, such as foot and mouth disease virus (FMDV), an important animal pathogen. Antigenic variants of FMDV have been classically diagnosed by immunological or RT-PCR-based methods. DNA microarrays are becoming increasingly useful for the analysis of gene expression and single nucleotide polymorphisms (SNPs). Recently, a FMDV microarray was described to detect simultaneously the seven FMDV serotypes. These results encourage the development of new oligonucleotide microarrays to probe the fine genetic and antigenic composition of FMDV for diagnosis, vaccine design, and to gain insight into the molecular epidemiology of this pathogen. RESULTS: A FMDV microarray was designed and optimized to detect SNPs at a major antigenic site of the virus. A screening of point mutants of the genomic region encoding antigenic site A of FMDV C-S8c1 was achieved. The hybridization pattern of a mutant includes specific positive and negative signals as well as crosshybridization signals, which are of different intensity depending on the thermodynamic stability of each probe-target pair. Moreover, an array bioinformatic classification method was developed to evaluate the hybridization signals. This statistical analysis shows that the procedure allows a very accurate classification per variant genome. CONCLUSION: A specific approach based on a microarray platform aimed at distinguishing point mutants within an important determinant of antigenicity and host cell tropism, namely the G-H loop of capsid protein VP1, was developed. The procedure is of general applicability as a test for specificity and discriminatory power of microarray-based diagnostic procedures using multiple oligonucleotide probes.

Safety assessment by in vitro digestibility and allergenicity of genetically modified maize with an amaranth 11S globulin.

Prospective testing for allergenicity of proteins obtained from sources with no prior history of causing allergy has been difficult to perform. Thus, the objective of this work was to assess the food safety of genetically modified maize with an amaranth globulin protein termed amarantin. Transgenic maize lines evaluated showed, in relation to nontransgenic, 4-35% more protein and 0-44% higher contents of specific essential amino acids. Individual sequence analysis with known amino acid sequences, reported as allergens, showed that none of these IgE elicitors were identified in amarantin. Amarantin was digested within the first 15 min by Simulated Gastric Fluid treatment as observed by Western blot. Expressed amarantin did not induce important levels of specific IgE antibodies in BALB/c mice, as analyzed by ELISA. We conclude that the transgenic maize with amarantin is not an important allergenicity inducer, just as nontransgenic maize.

CAFASP3: the third critical assessment of fully automated structure prediction methods.

We present the results of the fully automated CAFASP3 experiment, which was carried out in parallel with CASP5, using the same set of prediction targets. CAFASP participation is restricted to fully automatic structure prediction servers. The servers' performance is evaluated by using previously announced, objective, reproducible and fully automated evaluation methods. More than 60 servers participated in CAFASP3, covering all categories of structure prediction. As in the previous CAFASP2 experiment, it was possible to identify a group of 5-10 top performing independent servers. This group of top performing independent servers produced relatively accurate models for all the 32 "Homology Modeling" targets, and for up to 43% of the 30 "Fold Recognition" targets. One of the most important results of CAFASP3 was the realization of the value of all the independent servers as a group, as evidenced by the superior performance of "meta-predictors" (defined here as predictors that make use of the output of other CAFASP servers). The performance of the best automated meta-predictors was roughly 30% higher than that of the best independent server. More significantly, the performance of the best automated meta-predictors was comparable with that of the best 5-10 human CASP predictors. This result shows that significant progress has been achieved in automatic structure prediction and has important implications to the prospects of automated structure modeling in the context of structural genomics.