Multi-Omics and Pathway analyses of Genome-Wide associations Implicate Regulation and Immunity in Verbal Declarative Memory Performance

BACKGROUND: Uncovering the functional relevance underlying verbal declarative memory (VDM) genome-wide association study (GWAS) results may facilitate the development of interventions to reduce age-related memory decline and dementia. METHODS: We performed multi-omics and pathway enrichment analyses of paragraph (PAR-dr) and word list (WL-dr) delayed recall GWAS from 29,076 older non-demented individuals of European descent. We assessed the relationship between single-variant associations and expression quantitative trait loci (eQTLs) in 44 tissues and methylation quantitative trait loci (meQTLs) in the hippocampus. We determined the relationship between gene associations and transcript levels in 53 tissues, annotation as immune genes, and regulation by transcription factors (TFs) and microRNAs. to identify significant pathways, gene set enrichment was tested in each cohort and meta-analyzed across cohorts. Analyses of differential expression in brain tissues were conducted for pathway component genes. RESULTS: The single-variant associations of VDM showed significant linkage disequilibrium (LD) with eQTLs across all tissues and meQTLs within the hippocampus. Stronger WL-dr gene associations correlated with reduced expression in four brain tissues, including the hippocampus. More robust PAR-dr and/or WL-dr gene associations were intricately linked with immunity and were influenced by 31 TFs and 2 microRNAs. Six pathways, including type I diabetes, exhibited significant associations with both PAR-dr and WL-dr. These pathways included fifteen MHC genes intricately linked to VDM performance, showing diverse expression patterns based on cognitive status in brain tissues. CONCLUSIONS: VDM genetic associations influence expression regulation via eQTLs and meQTLs. The involvement of TFs, microRNAs, MHC genes, and immune-related pathways contributes to VDM performance in older individuals

Brain-age prediction:Systematic evaluation of site effects, and sample age range and size

Structural neuroimaging data have been used to compute an estimate of the biological age of the brain (brain-age) which has been associated with other biologically and behaviorally meaningful measures of brain development and aging. The ongoing research interest in brain-age has highlighted the need for robust and publicly available brain-age models pre-trained on data from large samples of healthy individuals. To address this need we have previously released a developmental brain-age model. Here we expand this work to develop, empirically validate, and disseminate a pre-trained brain-age model to cover most of the human lifespan. To achieve this, we selected the best-performing model after systematically examining the impact of seven site harmonization strategies, age range, and sample size on brain-age prediction in a discovery sample of brain morphometric measures from 35,683 healthy individuals (age range: 5–90 years; 53.59% female). The pre-trained models were tested for cross-dataset generalizability in an independent sample comprising 2101 healthy individuals (age range: 8–80 years; 55.35% female) and for longitudinal consistency in a further sample comprising 377 healthy individuals (age range: 9–25 years; 49.87% female). This empirical examination yielded the following findings: (1) the accuracy of age prediction from morphometry data was higher when no site harmonization was applied; (2) dividing the discovery sample into two age-bins (5–40 and 40–90 years) provided a better balance between model accuracy and explained age variance than other alternatives; (3) model accuracy for brain-age prediction plateaued at a sample size exceeding 1600 participants. These findings have been incorporated into CentileBrain (https://centilebrain.org/#/brainAGE2), an open-science, web-based platform for individualized neuroimaging metrics.</p

Industrial Relations Experiments in China: Balancing Equity and Efficiency the Chinese Way

China should build socialism by "crossing the river by feeling for stones" (Deng Xiaoping). Chinese industrial relations are changing accordingly. Local union experiments have implemented local-level changes experimenting with institutional reforms that address efficiency and equity imbalances. Local union leaders have exercised autonomy to develop multi-employer “community unions” in Changchun’s Chaoyang District to represent peasant migrant workers employed by small firms by targeting small geographic zones and hiring union presidents as organizers, aggregating union members into amalgamated units. While the union’s role still includes social harmonization, unions have undertaken an additional representative role. Similar efforts elsewhere have given the union representation experience. Unions have organized multi-employer federations across industries. Unions also have collaborated with local governments on innovative structures to ensure that companies in some industries, such as construction, post a “bond” to guarantee end-of-year compensation. Finally, this paper discusses the role of the new Labor Contract Law in institutionalizing these changes. The LCL defines more precisely employment relationships and workers’ legal rights and seems to increase unions’ legal authority to ensure that employers respect individual workers’ rights, supports the extension of collective contracts to more enterprises, and appears to give unions greater authority to represent workers within the employment relationship and before legal authorities. These changes may provide a material basis for balancing efficiency with equity. We think these experiments have political foundations, whether it is “harmonious society” or simply to extend the union’s organizing maintain political status. Further research will determine whether these experiments are successful

Genome-wide meta-analyses reveal novel loci for verbal short-term memory and learning

Understanding the genomic basis of memory processes may help in combating neurodegenerative disorders. Hence, we examined the associations of common genetic variants with verbal short-term memory and verbal learning in adults without dementia or stroke (N = 53,637). We identified novel loci in the intronic region of CDH18, and at 13q21 and 3p21.1, as well as an expected signal in the APOE/APOC1/TOMM40 region. These results replicated in an independent sample. Functional and bioinformatic analyses supported many of these loci and further implicated POC1. We showed that polygenic score for verbal learning associated with brain activation in right parieto-occipital region during working memory task. Finally, we showed genetic correlations of these memory traits with several neurocognitive and health outcomes. Our findings suggest a role of several genomic loci in verbal memory processes.Peer reviewe

Short-term local expression of a PD-L1 blocking antibody from a self-replicating RNA vector induces potent antitumor responses

Immune checkpoint blockade has shown anti-cancer efficacy, but requires systemic administration of monoclonal antibodies (mAbs), often leading to adverse effects. To avoid toxicity, mAbs could be expressed locally in tumors. We developed adeno-associated virus (AAV) and Semliki Forest virus (SFV) vectors expressing anti-programmed death ligand 1 (aPDL1) mAb. When injected intratumorally in MC38 tumors, both viral vectors led to similar local mAb expression at 24 h, diminishing quickly in SFV-aPDL1-treated tumors. However, SFV-aPDL1 induced >40% complete regressions and was superior to AAV-aPDL1, as well as to aPDL1 mAb given systemically or locally. SFV-aPDL1 induced abscopal effects and was also efficacious against B16-ovalbumin (OVA). The higher SFV-aPDL1 antitumor activity could be related to local upregulation of interferon-stimulated genes because of SFV RNA replication. This was confirmed by combining local SFV-LacZ administration and systemic aPDL1 mAb, which provided higher antitumor effects than each separated agent. SFVaPDL1 promoted tumor-specific CD8 T cells infiltration in both tumor models. In MC38, SFV-aPDL1 upregulated co-stimulatory markers (CD137/OX40) in tumor CD8 T cells, and its combination with anti-CD137 mAb showed more pronounced antitumor effects than each single agent. These results indicate that local transient expression of immunomodulatory mAbs using non-propagative RNA vectors inducing type I interferon (IFN-I) responses represents a potent and

External Learning Opportunities and the Diffusion of Process Innovations to Small Firms: The Case of Programmable Automation

In this chapter, we are concerned with explaining which types of firms have failed to adopt well-known improvements in process technology. This problem has, of course, been the underlying concern of all studies of diffusion “to rationalize why, if a new technology is superior, it is not taken up by all potential adopters” (Stoneman, 1983). Drawing on various theoretical perspectives, we identify a number of different barriers to adoption. With data collected from a 1987 nationally representative sample of US establishments in 21 metal-working and machinery manufacturing industries, we then construct a multivariate logistic regression model to empirically test for the effects of these factors on the likelihood of adoption of a particular process innovation, namely programmable automation (PA) machine tools

Understanding the Return of Genomic Sequencing Results Process: Content Review of Participant Summary Letters in the eMERGE Research Network

A challenge in returning genomic test results to research participants is how best to communicate complex and clinically nuanced findings to participants in a manner that is scalable to the large numbers of participants enrolled. The purpose of this study was to examine the features of genetic results letters produced at each Electronic Medical Records and Genomics (eMERGE3) Network site to assess their readability and content. Letters were collected from each site, and a qualitative analysis of letter content and a quantitative analysis of readability statistics were performed. Because letters were produced independently at each eMERGE site, significant heterogeneity in readability and content was found. The content of letters varied widely from a baseline of notifying participants that results existed to more detailed information about positive or negative results, as well as materials for sharing with family members. Most letters were significantly above the Centers for Disease Control-suggested reading level for health communication. While continued effort should be applied to make letters easier to understand, the ongoing challenge of explaining complex genomic information, the implications of negative test results, and the uncertainty that comes with some types of test and result makes simplifying letter text challenging

Brain‐age prediction: systematic evaluation of site effects, and sample age range and size

Structural neuroimaging data have been used to compute an estimate of the biological age of the brain (brain‐age) which has been associated with other biologically and behaviorally meaningful measures of brain development and aging. The ongoing research interest in brain‐age has highlighted the need for robust and publicly available brain‐age models pre‐trained on data from large samples of healthy individuals. To address this need we have previously released a developmental brain‐age model. Here we expand this work to develop, empirically validate, and disseminate a pre‐trained brain‐age model to cover most of the human lifespan. To achieve this, we selected the best‐performing model after systematically examining the impact of seven site harmonization strategies, age range, and sample size on brain‐age prediction in a discovery sample of brain morphometric measures from 35,683 healthy individuals (age range: 5–90 years; 53.59% female). The pre‐trained models were tested for cross‐dataset generalizability in an independent sample comprising 2101 healthy individuals (age range: 8–80 years; 55.35% female) and for longitudinal consistency in a further sample comprising 377 healthy individuals (age range: 9–25 years; 49.87% female). This empirical examination yielded the following findings: (1) the accuracy of age prediction from morphometry data was higher when no site harmonization was applied; (2) dividing the discovery sample into two age‐bins (5–40 and 40–90 years) provided a better balance between model accuracy and explained age variance than other alternatives; (3) model accuracy for brain‐age prediction plateaued at a sample size exceeding 1600 participants. These findings have been incorporated into CentileBrain (https://centilebrain.org/#/brainAGE2), an open‐science, web‐based platform for individualized neuroimaging metrics