Variation in Establishment Success for American Mistletoe [\u3ci\u3ePhoradendron leucarpum\u3c/i\u3e (Raf.) Reveal & M.C. Johnst. (Viscaceae)] Appears Most Likely to Predict its Distribution in Virginia and North Carolina, United States

Dispersal limitation and variation in habitat suitability may determine an association of American mistletoe [Phoradendron leucarpum (Raf.) Reveal & M.C. Johnst. (Viscaceae)] with forested wetlands in Virginia and North Carolina, United States. Here, we first tested the alternative hypothesis that variation in host availability drives this habitat relationship. We used a generalized linear model to show a positive effect of forested wetland habitat on American mistletoe occurrence after accounting for both variation in host availability and differences among regions in host use. We then used seed sowing experiments to quantify how light availability and flood regime determine the viability of American mistletoe, allowing us to evaluate the potential for establishment limitation to determine this habitat relationship. Light availability predicted establishment rates but % canopy openness did not predict seed germination rates. Thus, variation in the ability for American mistletoe to establish across forested habitat types with different local light availabilities is a potentially important mechanism in determining its distribution

Chromosome 15q25 (CHRNA3-CHRNA5) Variation Impacts Indirectly on Lung Cancer Risk

Genetic variants at the 15q25 CHRNA5-CHRNA3 locus have been shown to influence lung cancer risk however there is controversy as to whether variants have a direct carcinogenic effect on lung cancer risk or impact indirectly through smoking behavior. We have performed a detailed analysis of the 15q25 risk variants rs12914385 and rs8042374 with smoking behavior and lung cancer risk in 4,343 lung cancer cases and 1,479 controls from the Genetic Lung Cancer Predisposition Study (GELCAPS). A strong association between rs12914385 and rs8042374, and lung cancer risk was shown, odds ratios (OR) were 1.44, (95% confidence interval (CI): 1.29–1.62, P = 3.69×10−10) and 1.35 (95% CI: 1.18–1.55, P = 9.99×10−6) respectively. Each copy of risk alleles at rs12914385 and rs8042374 was associated with increased cigarette consumption of 1.0 and 0.9 cigarettes per day (CPD) (P = 5.18×10−5 and P = 5.65×10−3). These genetically determined modest differences in smoking behavior can be shown to be sufficient to account for the 15q25 association with lung cancer risk. To further verify the indirect effect of 15q25 on the risk, we restricted our analysis of lung cancer risk to never-smokers and conducted a meta-analysis of previously published studies of lung cancer risk in never-smokers. Never-smoker studies published in English were ascertained from PubMed stipulating - lung cancer, risk, genome-wide association, candidate genes. Our study and five previously published studies provided data on 2,405 never-smoker lung cancer cases and 7,622 controls. In the pooled analysis no association has been found between the 15q25 variation and lung cancer risk (OR = 1.09, 95% CI: 0.94–1.28). This study affirms the 15q25 association with smoking and is consistent with an indirect link between genotype and lung cancer risk

Federated learning enables big data for rare cancer boundary detection.

Although machine learning (ML) has shown promise across disciplines, out-of-sample generalizability is concerning. This is currently addressed by sharing multi-site data, but such centralization is challenging/infeasible to scale due to various limitations. Federated ML (FL) provides an alternative paradigm for accurate and generalizable ML, by only sharing numerical model updates. Here we present the largest FL study to-date, involving data from 71 sites across 6 continents, to generate an automatic tumor boundary detector for the rare disease of glioblastoma, reporting the largest such dataset in the literature (n = 6, 314). We demonstrate a 33% delineation improvement for the surgically targetable tumor, and 23% for the complete tumor extent, over a publicly trained model. We anticipate our study to: 1) enable more healthcare studies informed by large diverse data, ensuring meaningful results for rare diseases and underrepresented populations, 2) facilitate further analyses for glioblastoma by releasing our consensus model, and 3) demonstrate the FL effectiveness at such scale and task-complexity as a paradigm shift for multi-site collaborations, alleviating the need for data-sharing

Author Correction: Federated learning enables big data for rare cancer boundary detection.

10.1038/s41467-023-36188-7NATURE COMMUNICATIONS14

Federated Learning Enables Big Data for Rare Cancer Boundary Detection

Although machine learning (ML) has shown promise across disciplines, out-of-sample generalizability is concerning. This is currently addressed by sharing multi-site data, but such centralization is challenging/infeasible to scale due to various limitations. Federated ML (FL) provides an alternative paradigm for accurate and generalizable ML, by only sharing numerical model updates. Here we present the largest FL study to-date, involving data from 71 sites across 6 continents, to generate an automatic tumor boundary detector for the rare disease of glioblastoma, reporting the largest such dataset in the literature (n = 6, 314). We demonstrate a 33% delineation improvement for the surgically targetable tumor, and 23% for the complete tumor extent, over a publicly trained model. We anticipate our study to: 1) enable more healthcare studies informed by large diverse data, ensuring meaningful results for rare diseases and underrepresented populations, 2) facilitate further analyses for glioblastoma by releasing our consensus model, and 3) demonstrate the FL effectiveness at such scale and task-complexity as a paradigm shift for multi-site collaborations, alleviating the need for data-sharing

Key seabird areas in southern New England identified using a community occupancy model

Seabirds are of conservation concern, and as new potential risks to seabirds are arising, the need to provide unbiased estimates of species\u27 distributions is growing. We applied community occupancy models to detection/non-detection data collected from repeated aerial striptransect surveys conducted in 2 large study plots off southern New England, USA; one off the coast of Rhode Island and the other in Nantucket Sound. A total of 17 seabird species were observed at least once in each study plot. We found that detection varied by survey date and effort for most species and the average detection probability across species was less than 0.4. We estimated the influence of water depth, sea surface temperature, and sea surface chl a concentration on species-specific occupancy. Diving species showed large differences between the 2 study plots in their predicted winter distributions, which were largely explained by water depth acting as a stronger predictor of occupancy in Rhode Island than in Nantucket Sound. Conversely, similarities between the 2 study plots in predicted winter distributions of surface-feeding species were explained by sea surface temperature or chlorophyll a concentration acting as predictors of these species\u27 occupancy in both study plots. We predicted the number of species at each site using the observed data in order to detect \u27hot-spots\u27 of seabird diversity and use in the 2 study plots. These results provide new information on detection of species, areas of use, and relationships with environmental variables that will be valuable for biologists and planners interested in seabird conservation in the region