The Economic Consequences of Social-Network Structure

We survey the literature on the economic consequences of the structure of social networks. We develop a taxonomy of "macro" and "micro" characteristics of social-interaction networks and discuss both the theoretical and empirical findings concerning the role of those characteristics in determining learning, diffusion, decisions, and resulting behaviors. We also discuss the challenges of accounting for the endogeneity of networks in assessing the relationship between the patterns of interactions and behaviors

Nonsteroidal anti-inflammatory drug use and Alzheimer's disease risk: the MIRAGE Study

BACKGROUND: Nonsteroidal anti-inflammatory drugs (NSAID) use may protect against Alzheimer's disease (AD) risk. We sought examine the association between NSAID use and risk of AD, and potential effect modification by APOE-ε4 carrier status and ethnicity. METHODS: The MIRAGE Study is a multi-center family study of genetic and environmental risk factors for AD. Subjects comprised 691 AD patients (probands) and 973 family members enrolled at 15 research centers between 1996 and 2002. The primary independent and dependent variables were prior NSAID use and AD case status, respectively. We stratified the dataset in order to evaluate whether the association between NSAID use and AD was similar in APOE-ε4 carriers and non-carriers. Ethnicity was similarly examined as an effect modifier. RESULTS: NSAID use was less frequent in cases compared to controls in the overall sample (adjusted OR = 0.64; 95% CI = 0.38–1.05). The benefit of NSAID use appeared more pronounced among APOE-ε4 carriers (adjusted OR = 0.49; 95% CI = 0.24–0.98) compared to non-carriers, although this association was not statistically significant. The pattern of association was similar in Caucasian and African Americans. CONCLUSIONS: NSAID use is inversely associated with AD and may be modified by APOE genotype. Prospective studies and clinical trials of sufficient power to detect effect modification by APOE-ε4 carrier status are needed

Adjuvant nab-Paclitaxel + Gemcitabine in Resected Pancreatic Ductal Adenocarcinoma: Results From a Randomized, Open-Label, Phase III Trial

PURPOSE: This randomized, open -label trial compared the efficacy and safety of adjuvant nabpaclitaxel + gemcitabine with those of gemcitabine for resected pancreatic ductal adenocarcinoma (ClinicalTrials.gov identifier: NCT01964430). METHODS: We assigned 866 treatment -naive patients with pancreatic ductal adenocarcinoma to nab-paclitaxel (125 mg/m2) + gemcitabine (1,000 mg/m(2)) or gemcitabine alone to one 30-40 infusion on days 1, 8, and 15 of six 28 -day cycles. The primary end point was independently assessed disease -free survival (DFS). Additional end points included investigator-assessed DFS, overall survival (OS), and safety. RESULTS: Two hundred eighty-seven of 432 patients and 310 of 434 patients completed nabpaclitaxel + gemcitabine and gemcitabine treatment, respectively. At primary data cutoff (December 31, 2018; median follow-up, 38.5 [interquartile range [IQR], 33.8-43 months), the median independently assessed DFS was 19.4 (nab-paclitaxel + gemcitabine) versus 18.8 months (gemcitabine; hazard ratio [HR], 0.88; 95% CI, 0.729 to 1.063; P =.18). The median investigator-assessed DFS was 16.6 (IQR, 8.4-47.0) and 13.7 (IQR, 8.3-44.1) months, respectively (HR, 0.82; 95% CI, 0.694 to 0.965; P=.02). The median OS (427 events; 68% mature) was 40.5 (IQR, 20.7 to not reached) and 36.2 (IQR, 17.7-53.3) months, respectively (HR, 0.82; 95% CI, 0.680 to 0.996; P =.045). At a 16 -month follow-up (cutoff, April 3, 2020; median follow-up, 51.4 months [IQR, 47.0-57.0]), the median OS (511 events; 81% mature) was 41.8 (nab-paclitaxel + gemcitabine) versus 37.7 months (gemcitabine; HR, 0.82; 95% CI, 0.687 to 0.973; P =.0232). At the 5 -year follow-up (cutoff, April 9, 2021; median follow-up, 63.2 months [IQR, 60.1-68.7]), the median OS (555 events; 88% mature) was 41.8 versus 37.7 months, respectively (HR, 0.80; 95% CI, 0.678 to 0.947; P =.0091). Eighty-six percent (nab-paclitaxel + gemcitabine) and 68% (gemcitabine) of patients experienced grade >= 3 treatment -emergent adverse events. Two patients per study arm died of treatment -emergent adverse events. CONCLUSION: The primary end point (independently assessed DFS) was not met despite favorable OS seen with nab-paclitaxel + gemcitabine

ADAGE signature analysis: differential expression analysis with data-defined gene sets

Abstract Background Gene set enrichment analysis and overrepresentation analyses are commonly used methods to determine the biological processes affected by a differential expression experiment. This approach requires biologically relevant gene sets, which are currently curated manually, limiting their availability and accuracy in many organisms without extensively curated resources. New feature learning approaches can now be paired with existing data collections to directly extract functional gene sets from big data. Results Here we introduce a method to identify perturbed processes. In contrast with methods that use curated gene sets, this approach uses signatures extracted from public expression data. We first extract expression signatures from public data using ADAGE, a neural network-based feature extraction approach. We next identify signatures that are differentially active under a given treatment. Our results demonstrate that these signatures represent biological processes that are perturbed by the experiment. Because these signatures are directly learned from data without supervision, they can identify uncurated or novel biological processes. We implemented ADAGE signature analysis for the bacterial pathogen Pseudomonas aeruginosa. For the convenience of different user groups, we implemented both an R package (ADAGEpath) and a web server ( http://adage.greenelab.com ) to run these analyses. Both are open-source to allow easy expansion to other organisms or signature generation methods. We applied ADAGE signature analysis to an example dataset in which wild-type and ∆anr mutant cells were grown as biofilms on the Cystic Fibrosis genotype bronchial epithelial cells. We mapped active signatures in the dataset to KEGG pathways and compared with pathways identified using GSEA. The two approaches generally return consistent results; however, ADAGE signature analysis also identified a signature that revealed the molecularly supported link between the MexT regulon and Anr. Conclusions We designed ADAGE signature analysis to perform gene set analysis using data-defined functional gene signatures. This approach addresses an important gap for biologists studying non-traditional model organisms and those without extensive curated resources available. We built both an R package and web server to provide ADAGE signature analysis to the community

Additional file 3: Figure S3. of ADAGE signature analysis: differential expression analysis with data-defined gene sets

Validation of Node35pos as a transcriptional program via the STRING network. A: The largest connected module of the gene-gene network subset by genes in Node35pos. B: Gene-gene networks returned by STRING when searching PA2486, PA3229, and PA4881 respectively. The STRING networks of the three genes are subsets of the Node35pos network. (TIFF 2780 kb

Additional file 1: Figure S1. of ADAGE signature analysis: differential expression analysis with data-defined gene sets

The relationship between corruption level used in building ADAGE models and the redundancy of signatures derived from the models. The plot summarized results from 100 ADAGE models built at each corruption level. A: The number of signature pairs with gene compositions significantly overlapped increases with corruption level until the corruption level reaches 30%. B: As corruption level increases, the number of signatures in a model that enriched of the same KEGG pathway also increases on average, indicating the signatures become more redundant. C: ADAGE models tend to capture more unique KEGG pathways (pathway coverage) when more noise was added during training until the corruption level is higher than 25%. (TIFF 903 kb

Additional file 4: Figure S4. of ADAGE signature analysis: differential expression analysis with data-defined gene sets

Groups of signatures that are uncharacterized by KEGG. A: The signature similarity heatmap of uncharacterized signatures. Heatmap color reflects the odds ratio that two signatures overlap in their gene contents. Signatures are divided into two groups based on their similarity. B: The largest connected module in the gene-gene network subset by genes in Group1 signatures. This module contains the MexT regulatory program. C: The largest connected module in the gene-gene network subset by genes in Group2 signatures. This module contains many genes involved in quorum sensing. (TIFF 3586 kb