Neighborhood Effects on the Long-Term Well-Being of Low-Income Adults

Nearly 9 million Americans live in extreme-poverty neighborhoods, places that also tend to be racially segregated and dangerous. Yet, the effects on the well-being of residents of moving out of such communities into less distressed areas remain uncertain. Using data from Moving to Opportunity, a unique randomized housing mobility experiment, we found that moving from a high-poverty to lower-poverty neighborhood leads to long-term (10- to 15-year) improvements in adult physical and mental health and subjective well-being, despite not affecting economic self-sufficiency. A 1–standard deviation decline in neighborhood poverty (13 percentage points) increases subjective well-being by an amount equal to the gap in subjective well-being between people whose annual incomes differ by $13,000—a large amount given that the average control group income is$20,000. Subjective well-being is more strongly affected by changes in neighborhood economic disadvantage than racial segregation, which is important because racial segregation has been declining since 1970, but income segregation has been increasing

Thermal Density Functional Theory in Context

This chapter introduces thermal density functional theory, starting from the ground-state theory and assuming a background in quantum mechanics and statistical mechanics. We review the foundations of density functional theory (DFT) by illustrating some of its key reformulations. The basics of DFT for thermal ensembles are explained in this context, as are tools useful for analysis and development of approximations. We close by discussing some key ideas relating thermal DFT and the ground state. This review emphasizes thermal DFT's strengths as a consistent and general framework.Comment: Submitted to Spring Verlag as chapter in "Computational Challenges in Warm Dense Matter", F. Graziani et al. ed

A Computational Investigation on the Connection between Dynamics Properties of Ribosomal Proteins and Ribosome Assembly

Assembly of the ribosome from its protein and RNA constituents has been studied extensively over the past 50 years, and experimental evidence suggests that prokaryotic ribosomal proteins undergo conformational changes during assembly. However, to date, no studies have attempted to elucidate these conformational changes. The present work utilizes computational methods to analyze protein dynamics and to investigate the linkage between dynamics and binding of these proteins during the assembly of the ribosome. Ribosomal proteins are known to be positively charged and we find the percentage of positive residues in r-proteins to be about twice that of the average protein: Lys+Arg is 18.7% for E. coli and 21.2% for T. thermophilus. Also, positive residues constitute a large proportion of RNA contacting residues: 39% for E. coli and 46% for T. thermophilus. This affirms the known importance of charge-charge interactions in the assembly of the ribosome. We studied the dynamics of three primary proteins from E. coli and T. thermophilus 30S subunits that bind early in the assembly (S15, S17, and S20) with atomic molecular dynamic simulations, followed by a study of all r-proteins using elastic network models. Molecular dynamics simulations show that solvent-exposed proteins (S15 and S17) tend to adopt more stable solution conformations than an RNA-embedded protein (S20). We also find protein residues that contact the 16S rRNA are generally more mobile in comparison with the other residues. This is because there is a larger proportion of contacting residues located in flexible loop regions. By the use of elastic network models, which are computationally more efficient, we show that this trend holds for most of the 30S r-proteins

Can Social Policies Improve Health? A Systematic Review and Meta-Analysis of 38 Randomized Trials.

Policy Points Social policies might not only improve economic well-being, but also health. Health policy experts have therefore advocated for investments in social policies both to improve population health and potentially reduce health system costs. Since the 1960s, a large number of social policies have been experimentally evaluated in the United States. Some of these experiments include health outcomes, providing a unique opportunity to inform evidence-based policymaking. Our comprehensive review and meta-analysis of these experiments find suggestive evidence of health benefits associated with investments in early life, income support, and health insurance interventions. However, most studies were underpowered to detect health outcomes. CONTEXT: Insurers and health care providers are investing heavily in nonmedical social interventions in an effort to improve health and potentially reduce health care costs. METHODS: We performed a systematic review and meta-analysis of all known randomized social experiments in the United States that included health outcomes. We reviewed 5,880 papers, reports, and data sources, ultimately including 61 publications from 38 randomized social experiments. After synthesizing the main findings narratively, we conducted risk of bias analyses, power analyses, and random-effects meta-analyses where possible. Finally, we used multivariate regressions to determine which study characteristics were associated with statistically significant improvements in health outcomes. FINDINGS: The risk of bias was low in 17 studies, moderate in 11, and high in 33. Of the 451 parameter estimates reported, 77% were underpowered to detect health outcomes. Among adequately powered parameters, 49% demonstrated a significant health improvement, 44% had no effect on health, and 7% were associated with significant worsening of health. In meta-analyses, early life and education interventions were associated with a reduction in smoking (odds ratio [OR] = 0.92, 95% confidence interval [CI] 0.86-0.99). Income maintenance and health insurance interventions were associated with significant improvements in self-rated health (OR = 1.20, 95% CI 1.06-1.36, and OR = 1.38, 95% CI 1.10-1.73, respectively), whereas some welfare-to-work interventions had a negative impact on self-rated health (OR = 0.77, 95% CI 0.66-0.90). Housing and neighborhood trials had no effect on the outcomes included in the meta-analyses. A positive effect of the trial on its primary socioeconomic outcome was associated with higher odds of reporting health improvements. We found evidence of publication bias for studies with null findings. CONCLUSIONS: Early life, income, and health insurance interventions have the potential to improve health. However, many of the included studies were underpowered to detect health effects and were at high or moderate risk of bias. Future social policy experiments should be better designed to measure the association between interventions and health outcomes

Is media multitasking good for cybersecurity? Exploring the relationship between media multitasking and everyday cognitive failures on self-reported risky cybersecurity behaviors

The current study focused on how engaging in media multitasking (MMT) and the experience of everyday cognitive failures impact on the individual's engagement in risky cybersecurity behaviors (RCsB). In total, 144 participants (32 males, 112 females) completed an online survey. The age range for participants was 18 to 43 years (M = 20.63, SD = 4.04). Participants completed three scales which included an inventory of weekly MMT, a measure of everyday cognitive failures, and RCsB. There was a significant difference between heavy media multitaskers (HMM), average media multitaskers (AMM), and light media multitaskers (LMM) in terms of RCsB, with HMM demonstrating more frequent risky behaviors than LMM or AMM. The HMM group also reported more cognitive failures in everyday life than the LMM group. A regression analysis showed that everyday cognitive failures and MMT acted as significant predictors for RCsB. These results expand our current understanding of the relationship between human factors and cybersecurity behaviors, which are useful to inform the design of training and intervention packages to mitigate RCsB

Molecular dynamics of ribosomal elongation factors G and Tu

Translation on the ribosome is controlled by external factors. During polypeptide lengthening, elongation factors EF-Tu and EF-G consecutively interact with the bacterial ribosome. EF-Tu binds and delivers an aminoacyl-tRNA to the ribosomal A site and EF-G helps translocate the tRNAs between their binding sites after the peptide bond is formed. These processes occur at the expense of GTP. EF-Tu:tRNA and EF-G are of similar shape, share a common binding site, and undergo large conformational changes on interaction with the ribosome. To characterize the internal motion of these two elongation factors, we used 25 ns long all-atom molecular dynamics simulations. We observed enhanced mobility of EF-G domains III, IV, and V and of tRNA in the EF-Tu:tRNA complex. EF-Tu:GDP complex acquired a configuration different from that found in the crystal structure of EF-Tu with a GTP analogue, showing conformational changes in the switch I and II regions. The calculated electrostatic properties of elongation factors showed no global similarity even though matching electrostatic surface patches were found around the domain I that contacts the ribosome, and in the GDP/GTP binding region

Labeled EF-Tus for rapid kinetic studies of pretranslocation complex formation

The universally conserved translation elongation factor EF-Tu delivers aminoacyl(aa)-tRNA in the form of an aa-tRNA·EF-Tu·GTP ternary complex (TC) to the ribosome where it binds to the cognate mRNA codon within the ribosomal A-site, leading to formation of a pretranslocation (PRE) complex. Here we describe preparation of QSY9 and Cy5 derivatives of the variant E348C-EF-Tu that are functional in translation elongation. Together with fluorophore derivatives of aa-tRNA and of ribosomal protein L11, located within the GTPase associated center (GAC), these labeled EF-Tus allow development of two new FRET assays that permit the dynamics of distance changes between EF-Tu and both L11 (Tu-L11 assay) and aa-tRNA (Tu-tRNA assay) to be determined during the decoding process. We use these assays to examine: (i) the relative rates of EF-Tu movement away from the GAC and from aa-tRNA during decoding, (ii) the effects of the misreading-inducing antibiotics streptomycin and paromomycin on tRNA selection at the A-site, and (iii) how strengthening the binding of aa-tRNA to EF-Tu affects the rate of EF-Tu movement away from L11 on the ribosome. These FRET assays have the potential to be adapted for high throughput screening of ribosomal antibiotics

Optimization of Ribosome Structure and Function by rRNA Base Modification

BACKGROUND: Translating mRNA sequences into functional proteins is a fundamental process necessary for the viability of organisms throughout all kingdoms of life. The ribosome carries out this process with a delicate balance between speed and accuracy. This work investigates how ribosome structure and function are affected by rRNA base modification. The prevailing view is that rRNA base modifications serve to fine tune ribosome structure and function. METHODOLOGY/PRINCIPAL FINDINGS: To test this hypothesis, yeast strains deficient in rRNA modifications in the ribosomal peptidyltransferase center were monitored for changes in and translational fidelity. These studies revealed allele-specific sensitivity to translational inhibitors, changes in reading frame maintenance, nonsense suppression and aa-tRNA selection. Ribosomes isolated from two mutants with the most pronounced phenotypic changes had increased affinities for aa-tRNA, and surprisingly, increased rates of peptidyltransfer as monitored by the puromycin assay. rRNA chemical analyses of one of these mutants identified structural changes in five specific bases associated with the ribosomal A-site. CONCLUSIONS/SIGNIFICANCE: Together, the data suggest that modification of these bases fine tune the structure of the A-site region of the large subunit so as to assure correct positioning of critical rRNA bases involved in aa-tRNA accommodation into the PTC, of the eEF-1A•aa-tRNA•GTP ternary complex with the GTPase associated center, and of the aa-tRNA in the A-site. These findings represent a direct demonstration in support of the prevailing hypothesis that rRNA modifications serve to optimize rRNA structure for production of accurate and efficient ribosomes

Evola: Ortholog database of all human genes in H-InvDB with manual curation of phylogenetic trees

Orthologs are genes in different species that evolved from a common ancestral gene by speciation. Currently, with the rapid growth of transcriptome data of various species, more reliable orthology information is prerequisite for further studies. However, detection of orthologs could be erroneous if pairwise distance-based methods, such as reciprocal BLAST searches, are utilized. Thus, as a sub-database of H-InvDB, an integrated database of annotated human genes (http://h-invitational.jp/), we constructed a fully curated database of evolutionary features of human genes, called ‘Evola’. In the process of the ortholog detection, computational analysis based on conserved genome synteny and transcript sequence similarity was followed by manual curation by researchers examining phylogenetic trees. In total, 18 968 human genes have orthologs among 11 vertebrates (chimpanzee, mouse, cow, chicken, zebrafish, etc.), either computationally detected or manually curated orthologs. Evola provides amino acid sequence alignments and phylogenetic trees of orthologs and homologs. In ‘dN/dS view’, natural selection on genes can be analyzed between human and other species. In ‘Locus maps’, all transcript variants and their exon/intron structures can be compared among orthologous gene loci. We expect the Evola to serve as a comprehensive and reliable database to be utilized in comparative analyses for obtaining new knowledge about human genes. Evola is available at http://www.h-invitational.jp/evola/

Specialist laboratory networks as preparedness and response tool - The emerging viral diseases-expert laboratory network and the chikungunya outbreak, Thailand, 2019

We illustrate the potential for specialist laboratory networks to be used as preparedness and response tool through rapid collection and sharing of data. Here, the Emerging Viral Diseases-Expert Laboratory Network (EVD-LabNet) and a laboratory assessment of chikungunya virus (CHIKV) in returning European travellers related to an ongoing outbreak in Thailand was used for this purpose. EVD-LabNet rapidly collected data on laboratory requests, diagnosed CHIKV imported cases and sequences generated, and shared among its members and with the European Centre for Disease Prevention and Control. Data across the network showed an increase in CHIKV imported cases during 1 October 2018-30 April 2019 vs the same period in 2018 (172 vs 50), particularly an increase in cases known to be related to travel to Thailand (72 vs 1). Moreover, EVD-LabNet showed that strains were imported from Thailand that cluster with strains of the ECSA-IOL E1 A226 variant emerging in Pakistan in 2016 and involved in the 2017 outbreaks in Italy. CHIKV diagnostic requests increased by 23.6% between the two periods. The impact of using EVD-LabNet or similar networks as preparedness and response tool could be improved by standardisation of the collection, quality and mining of data in routine laboratory management systems