Machine learning uncovers the most robust self-report predictors of relationship quality across 43 longitudinal couples studies

Given the powerful implications of relationship quality for health and well-being, a central mission of relationship science is explaining why some romantic relationships thrive more than others. This large-scale project used machine learning (i.e., Random Forests) to 1) quantify the extent to which relationship quality is predictable and 2) identify which constructs reliably predict relationship quality. Across 43 dyadic longitudinal datasets from 29 laboratories, the top relationship-specific predictors of relationship quality were perceived-partner commitment, appreciation, sexual satisfaction, perceived-partner satisfaction, and conflict. The top individual-difference predictors were life satisfaction, negative affect, depression, attachment avoidance, and attachment anxiety. Overall, relationship-specific variables predicted up to 45% of variance at baseline, and up to 18% of variance at the end of each study. Individual differences also performed well (21% and 12%, respectively). Actor-reported variables (i.e., own relationship-specific and individual-difference variables) predicted two to four times more variance than partner-reported variables (i.e., the partner’s ratings on those variables). Importantly, individual differences and partner reports had no predictive effects beyond actor-reported relationship-specific variables alone. These findings imply that the sum of all individual differences and partner experiences exert their influence on relationship quality via a person’s own relationship-specific experiences, and effects due to moderation by individual differences and moderation by partner-reports may be quite small. Finally, relationship-quality change (i.e., increases or decreases in relationship quality over the course of a study) was largely unpredictable from any combination of self-report variables. This collective effort should guide future models of relationships

Quantification of bottom trawl fishing damage to ancient shipwreck sites

Bottom trawl fishing presents a severe yet largely unquantified threat to shipwreck sites. Here we present a quantification of damage to sites from the Aegean and Black seas through high resolution imaging of 45 shipwrecks discovered by the E/V Nautilus expeditions, 2009-2012. These shipwrecks are part of a modern submarine landscape that is heavily damaged by trawls, which also remove sediment and smooth out natural features of the seabed. We quantify the severity of this threat to archaeological sites through repeat visits to one ancient shipwreck and quantify the change to the seabed over a period of eleven months. The results illustrate the benefits of enforced areas of restricted bottom trawling (Marine Protected Areas) to the in situ preservation of shipwreck sites and to natural seabed features and benthic habitats. Careful marine spatial planning and coordinated management of fishing activity can mitigate this destructive activity. In addition, we counter the claim made by some commercial salvors who use trawl damage as an excuse to salvage artifacts from wrecks, further destroying historically significant sites for profit

Simultaneous activation of p53 and inhibition of XIAP enhance the activation of apoptosis signaling pathways in AML

Activation of p53 by murine double minute (MDM2) antagonist nutlin-3a or inhibition of X-linked inhibitor of apoptosis (XIAP) induces apoptosis in acute myeloid leukemia (AML) cells. We demonstrate that concomitant inhibition of MDM2 by nutlin-3a and of XIAP by small molecule antagonists synergistically induced apoptosis in p53 wild-type OCI-AML3 and Molm13 cells. Knockdown of p53 by shRNA blunted the synergy, and down-regulation of XIAP by antisense oligonucleotide (ASO) enhanced nutlin-3a–induced apoptosis, suggesting that the synergy was mediated by p53 activation and XIAP inhibition. This is supported by data showing that inhibition of both MDM2 and XIAP by their respective ASOs induced significantly more cell death than either ASO alone. Importantly, p53 activation and XIAP inhibition enhanced apoptosis in blasts from patients with primary AML, even when the cells were protected by stromal cells. Mechanistic studies demonstrated that XIAP inhibition potentiates p53-induced apoptosis by decreasing p53-induced p21 and that p53 activation enhances XIAP inhibition-induced cell death by promoting mitochondrial release of second mitochondria-derived activator of caspases (SMAC) and by inducing the expression of caspase-6. Because both XIAP and p53 are presently being targeted in ongoing clinical trials in leukemia, the combination strategy holds promise for expedited translation into the clinic

The epidemiology of observed temperament: Factor structure and demographic group differences

This study investigated the factor structure of observational indicators of children’s temperament that were collected across the first three years of life in the Family Life Project (N = 1205) sample. A four-factor model (activity level, fear, anger, regulation), which corresponded broadly to Rothbart’s distinction between reactivity and regulation, provided an acceptable fit the observed data. Tests of measurement invariance demonstrated that a majority of the observational indicators exhibited comparable measurement properties for male vs. female, black vs. white, and poor vs. not-poor children, which improved the generalizability of these results. Unadjusted demographic group comparisons revealed small to moderate sized differences (Cohen ds = |.23 – .42|) in temperamental reactivity and moderate to large sized differences (Cohen ds = −.64 – −.97) in regulation. Collectively, demographic variables explained more of the variation in regulation (R(2) = .25) than in reactivity (R(2) = .02 – .06). Follow-up analyses demonstrated that race differences were substantially diminished in magnitude and better accounted for by poverty. These results help to validate the distinction between temperamental reactivity and regulation using observational indicators