Phylogenetic Relationships of Malvatheca (Bombacoideae and Malvoideae; Malvaceae sensu lato) as Inferred from Plastid DNA Sequences

Previous molecular phylogenetic analyses have revealed that elements of the former families Malvaceae sensu stricto and Bombacaceae together form a well-supported clade that has been named Malvatheca. Within Malvatheca, two major lineages have been observed; one, Bombacoideae, corresponds approximately to the palmate-leaved Bombacaceae, and the other, Malvoideae, includes the traditional Malvaceae (the mallows or Eumalvoideae). However, the composition of these two groups and their relationships to other elements of Malvatheca remain a source of uncertainty. Sequence data from two plastid regions, ndhF and trnK/matK, from 34 exemplars of Malvatheca and six outgroups were analyzed. Parsimony, likelihood, and Bayesian analyses of the sequence data provided a well-resolved phylogeny except that relationships among five lineages at the base of Malvatheca are poorly resolved. Nonetheless, a 6-bp insertion in matK suggests that Fremontodendreae is sister to the remainder of Malvatheca. Our results suggest that the Malvoideae originated in the Neotropics and that a mangrove taxon dispersed across the Pacific from South America to Australasia and later radiated out of Australasia to give rise to the ca. 1,700 living species of Eumalvoideae. Local clock analyses imply that the plastid genome underwent accelerated molecular evolution coincident with the dispersal out of the Americas and again with the radiation into the three major clades of Eumalvoideae

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

Genome-Wide Association Study of Susceptibility to Idiopathic Pulmonary Fibrosis

Rationale: Idiopathic pulmonary fibrosis (IPF) is a complex lung disease characterised by scarring of the lung that is believed to result from an atypical response to injury of the epithelium. Genome-wide association studies have reported signals of association implicating multiple pathways including host defence, telomere maintenance, signalling and cell-cell adhesion. Objectives: To improve our understanding of factors that increase IPF susceptibility by identifying previously unreported genetic associations. Methods and measurements: We conducted genome-wide analyses across three independent studies and meta-analysed these results to generate the largest genome-wide association study of IPF to date (2,668 IPF cases and 8,591 controls). We performed replication in two independent studies (1,456 IPF cases and 11,874 controls) and functional analyses (including statistical fine-mapping, investigations into gene expression and testing for enrichment of IPF susceptibility signals in regulatory regions) to determine putatively causal genes. Polygenic risk scores were used to assess the collective effect of variants not reported as associated with IPF. Main results: We identified and replicated three new genome-wide significant (P<5×10−8) signals of association with IPF susceptibility (associated with altered gene expression of KIF15, MAD1L1 and DEPTOR) and confirmed associations at 11 previously reported loci. Polygenic risk score analyses showed that the combined effect of many thousands of as-yet unreported IPF susceptibility variants contribute to IPF susceptibility. Conclusions: The observation that decreased DEPTOR expression associates with increased susceptibility to IPF, supports recent studies demonstrating the importance of mTOR signalling in lung fibrosis. New signals of association implicating KIF15 and MAD1L1 suggest a possible role of mitotic spindle-assembly genes in IPF susceptibility

Effects of ocean sprawl on ecological connectivity: impacts and solutions

The growing number of artificial structures in estuarine, coastal and marine environments is causing “ocean sprawl”. Artificial structures do not only modify marine and coastal ecosystems at the sites of their placement, but may also produce larger-scale impacts through their alteration of ecological connectivity - the movement of organisms, materials and energy between habitat units within seascapes. Despite the growing awareness of the capacity of ocean sprawl to influence ecological connectivity, we lack a comprehensive understanding of how artificial structures modify ecological connectivity in near- and off-shore environments, and when and where their effects on connectivity are greatest. We review the mechanisms by which ocean sprawl may modify ecological connectivity, including trophic connectivity associated with the flow of nutrients and resources. We also review demonstrated, inferred and likely ecological impacts of such changes to connectivity, at scales from genes to ecosystems, and potential strategies of management for mitigating these effects. Ocean sprawl may alter connectivity by: (1) creating barriers to the movement of some organisms and resources - by adding physical barriers or by modifying and fragmenting habitats; (2) introducing new structural material that acts as a conduit for the movement of other organisms or resources across the landscape; and (3) altering trophic connectivity. Changes to connectivity may, in turn, influence the genetic structure and size of populations, the distribution of species, and community structure and ecological functioning. Two main approaches to the assessment of ecological connectivity have been taken: (1) measurement of structural connectivity - the configuration of the landscape and habitat patches and their dynamics; and (2) measurement of functional connectivity - the response of organisms or particles to the landscape. Our review reveals the paucity of studies directly addressing the effects of artificial structures on ecological connectivity in the marine environment, particularly at large spatial and temporal scales. With the ongoing development of estuarine and marine environments, there is a pressing need for additional studies that quantify the effects of ocean sprawl on ecological connectivity. Understanding the mechanisms by which structures modify connectivity is essential if marine spatial planning and eco-engineering are to be effectively utilised to minimise impacts

Association study of human leukocyte antigen (HLA) variants and idiopathic pulmonary fibrosis

IntroductionIdiopathic pulmonary fibrosis (IPF) is a chronic interstitial pneumonia marked by progressive lung fibrosis and a poor prognosis. Recent studies have highlighted the potential role of infection in the pathogenesis of IPF and a prior association of theHLA-DQB1gene with idiopathic fibrotic interstitial pneumonia (including IPF) has been reported. Due to the important role that the Human Leukocyte Antigen (HLA) region plays in the immune response, here we evaluated if HLA genetic variation was associated specifically with IPF risk.MethodsWe performed a meta-analysis of associations of the HLA region with IPF risk in individuals of European ancestry from seven independent case-control studies of IPF (comprising a total of 5159 cases and 27 459 controls, including the prior study of fibrotic interstitial pneumonia). Single nucleotide polymorphisms, classical HLA alleles and amino acids were analysed and signals meeting a region-wide association thresholdp&lt;4.5×10−4and a posterior probability of replication &gt;90% were considered significant. We sought to replicate the previously reportedHLA-DQB1association in the subset of studies independent of the original report.ResultsThe meta-analysis of all seven studies identified four significant independent single nucleotide polymorphisms associated with IPF risk. However, none met the posterior probability for replication criterion. TheHLA-DQB1association was not replicated in the independent IPF studies.ConclusionVariation in the HLA region was not consistently associated with risk in studies of IPF. However, this does not preclude the possibility that other genomic regions linked to the immune response may be involved in the aetiology of IPF

X-Ray Psoralen Activated Cancer Therapy (X-PACT).

This work investigates X-PACT (X-ray Psoralen Activated Cancer Therapy): a new approach for the treatment of solid cancer. X-PACT utilizes psoralen, a potent anti-cancer therapeutic with current application to proliferative disease and extracorporeal photopheresis (ECP) of cutaneous T Cell Lymphoma. An immunogenic role for light-activated psoralen has been reported, contributing to long-term clinical responses. Psoralen therapies have to-date been limited to superficial or extracorporeal scenarios due to the requirement for psoralen activation by UVA light, which has limited penetration in tissue. X-PACT solves this challenge by activating psoralen with UV light emitted from novel non-tethered phosphors (co-incubated with psoralen) that absorb x-rays and re-radiate (phosphoresce) at UV wavelengths. The efficacy of X-PACT was evaluated in both in-vitro and in-vivo settings. In-vitro studies utilized breast (4T1), glioma (CT2A) and sarcoma (KP-B) cell lines. Cells were exposed to X-PACT treatments where the concentrations of drug (psoralen and phosphor) and radiation parameters (energy, dose, and dose rate) were varied. Efficacy was evaluated primarily using flow cell cytometry in combination with complimentary assays, and the in-vivo mouse study. In an in-vitro study, we show that X-PACT induces significant tumor cell apoptosis and cytotoxicity, unlike psoralen or phosphor alone (p<0.0001). We also show that apoptosis increases as doses of phosphor, psoralen, or radiation increase. Finally, in an in-vivo pilot study of BALBc mice with syngeneic 4T1 tumors, we show that the rate of tumor growth is slower with X-PACT than with saline or AMT + X-ray (p<0.0001). Overall these studies demonstrate a potential therapeutic effect for X-PACT, and provide a foundation and rationale for future studies. In summary, X-PACT represents a novel treatment approach in which well-tolerated low doses of x-ray radiation are delivered to a specific tumor site to generate UVA light which in-turn unleashes both short- and potentially long-term antitumor activity of photo-active therapeutics like psoralen

Telomere length associates with chronological age and mortality across racially diverse pulmonary fibrosis cohorts

Pulmonary fibrosis (PF) is characterized by profound scarring and poor survival. We investigated the association of leukocyte telomere length (LTL) with chronological age and mortality across racially diverse PF cohorts. LTL measurements among participants with PF stratified by race/ethnicity were assessed in relation to age and all-cause mortality, and compared to controls. Generalized linear models were used to evaluate the age-LTL relationship, Cox proportional hazards models were used for hazard ratio estimation, and the Cochran–Armitage test was used to assess quartiles of LTL. Standardized LTL shortened with increasing chronological age; this association in controls was strengthened in PF (R = −0.28; P < 0.0001). In PF, age- and sex-adjusted LTL below the median consistently predicted worse mortality across all racial groups (White, HR = 2.21, 95% CI = 1.79–2.72; Black, HR = 2.22, 95% CI = 1.05–4.66; Hispanic, HR = 3.40, 95% CI = 1.88–6.14; and Asian, HR = 2.11, 95% CI = 0.55–8.23). LTL associates uniformly with chronological age and is a biomarker predictive of mortality in PF across racial groups

Analysis of Forced Vital Capacity (FVC) trajectories in Idiopathic Pulmonary Fibrosis (IPF) identifies four distinct clusters of disease behaviour

Background: Idiopathic Pulmonary Fibrosis (IPF) is a progressive fibrotic lung disease with a variable clinical trajectory. Decline in Forced Vital Capacity (FVC) is the main indicator of progression, however missingness prevents long-term analysis of lung function patterns. We used Machine Learning (ML) techniques to identify patterns of lung function trajectory. Methods: Longitudinal FVC data were collected from 415 participants with IPF. The imputation performance of conventional and ML techniques to impute missing data was evaluated, then the fully imputed dataset was analysed by unsupervised clustering using Self-Organizing Maps (SOM). Anthropometrics, genomic associations, blood biomarkers and clinical outcomes were compared between clusters. Replication was performed using an independent dataset. Results: An unsupervised ML algorithm had the lowest imputation error amongst tested methods, and SOM identified four distinct clusters (CL1 to CL4), confirmed by sensitivity analysis. CL1 (n=140): linear decline over three years; CL2 (n=100): initial improvement in FVC before declining; CL3 (n=113): initial FVC decline before stabilisation; CL4(n=62): stable lung function. Median survival was shortest in CL1 (2.87 - 95%CI: 2.29–3.40) and longest in CL4 (5.65 - 95%CI: 5.18–6.62). Baseline FEV1/FVC ratio and biomarker SPD levels were significantly higher among clusters CL1 and CL3. Similar lung function clusters with some shared anthropometric characteristics were identified in the replication dataset. Conclusions: Using a data-driven unsupervised approach, we identified four clusters of lung function trajectory with distinct clinical and biochemical features. Enriching or stratifying longitudinal spirometric data into clusters may optimise evaluation of intervention efficacy during clinical trials and patient managemen