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1. Integration of survival data from multiple studies

   https://doi.org/10.1111/biom.13517  

   Ventz, Steffen; Mazumder, Rahul; Trippa, Lorenzo (September 2021, Biometrics)

   Abstract We introduce a statistical procedure that integrates datasets from multiple biomedical studies to predict patients' survival, based on individual clinical and genomic profiles. The proposed procedure accounts for potential differences in the relation between predictors and outcomes across studies, due to distinct patient populations, treatments and technologies to measure outcomes and biomarkers. These differences are modeled explicitly with study‐specific parameters. We use hierarchical regularization to shrink the study‐specific parameters towards each other and to borrow information across studies. The estimation of the study‐specific parameters utilizes a similarity matrix, which summarizes differences and similarities of the relations between covariates and outcomes across studies. We illustrate the method in a simulation study and using a collection of gene expression datasets in ovarian cancer. We show that the proposed model increases the accuracy of survival predictions compared to alternative meta‐analytic methods. 

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2. Community detection in the human connectome: Method types, differences and their impact on inference

   https://doi.org/10.1002/hbm.26669  

   Brooks, Skylar J.; Jones, Victoria O.; Wang, Haotian; Deng, Chengyuan; Golding, Staunton G. H.; Lim, Jethro; Gao, Jie; Daoutidis, Prodromos; Stamoulis, Catherine (March 2024, Human Brain Mapping)

   Abstract Community structure is a fundamental topological characteristic of optimally organized brain networks. Currently, there is no clear standard or systematic approach for selecting the most appropriate community detection method. Furthermore, the impact of method choice on the accuracy and robustness of estimated communities (and network modularity), as well as method‐dependent relationships between network communities and cognitive and other individual measures, are not well understood. This study analyzed large datasets of real brain networks (estimated from resting‐state fMRI from = 5251 pre/early adolescents in the adolescent brain cognitive development [ABCD] study), and = 5338 synthetic networks with heterogeneous, data‐inspired topologies, with the goal to investigate and compare three classes of community detection methods: (i) modularity maximization‐based (Newman and Louvain), (ii) probabilistic (Bayesian inference within the framework of stochastic block modeling (SBM)), and (iii) geometric (based on graph Ricci flow). Extensive comparisons between methods and their individual accuracy (relative to the ground truth in synthetic networks), and reliability (when applied to multiple fMRI runs from the same brains) suggest that the underlying brain network topology plays a critical role in the accuracy, reliability and agreement of community detection methods. Consistent method (dis)similarities, and their correlations with topological properties, were estimated across fMRI runs. Based on synthetic graphs, most methods performed similarly and had comparable high accuracy only in some topological regimes, specifically those corresponding to developed connectomes with at least quasi‐optimal community organization. In contrast, in densely and/or weakly connected networks with difficult to detect communities, the methods yielded highly dissimilar results, with Bayesian inference within SBM having significantly higher accuracy compared to all others. Associations between method‐specific modularity and demographic, anthropometric, physiological and cognitive parameters showed mostly method invariance but some method dependence as well. Although method sensitivity to different levels of community structure may in part explain method‐dependent associations between modularity estimates and parameters of interest, method dependence also highlights potential issues of reliability and reproducibility. These findings suggest that a probabilistic approach, such as Bayesian inference in the framework of SBM, may provide consistently reliable estimates of community structure across network topologies. In addition, to maximize robustness of biological inferences, identified network communities and their cognitive, behavioral and other correlates should be confirmed with multiple reliable detection methods. 

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3. A comparison of sex-specific senescence patterns in a long-lived marine mammal

   https://doi.org/10.3389/fevo.2024.1488373  

   Macdonald, Kaitlin R; Rotella, Jay J; Link, William A (January 2025, Frontiers in Ecology and Evolution)

   Gray, David A (Ed.)

   The lifetime fitness of an individual is determined by the integrated results of survival and reproduction. Improving our understanding of variation in survival senescence within and between species will therefore provide greater insight into the evolution of different life history strategies. Survival is influenced by multiple factors, consequently, variation in patterns of senescence is expected between individuals and sexes and across mating systems and the continuum of life history strategies. To date there is little consensus regarding the mechanisms driving the evolution of sex differences in actuarial senescence, necessitating the need for studies of sex-specific senescence for species across a wide range of life histories. The Weddell seal is a species of long-lived mammal that displays moderate polygyny and little sexual size dimorphism, which makes it an unusual species compared to other long-lived mammals that share the polygynous mating system. Here we used 37 years of data for 1,879 female and 1,474 male Weddell seals from Erebus Bay, Antarctica, to estimate and compare sex-specific patterns of survival rates using basis splines which allow flexible modeling of age-specific patterns. We found that males had lower rates of survival throughout life and higher rates of actuarial senescence after early adulthood compared to females. These results add to our understanding of sex-specific survival rates in the species and contribute information for a long-lived, polygynous species that should aid in achieving a broader understanding of aging between sexes and across the tree of life. 

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4. What is the meta‐analytic evidence for life‐history trade‐offs at the genetic level?

   https://doi.org/10.1111/ele.14354  

   Chang, Chia‐chen; Moiron, Maria; Sánchez‐Tójar, Alfredo; Niemelä, Petri T.; Laskowski, Kate L. (December 2023, Ecology Letters)

   Abstract Understanding the evolutionary mechanisms underlying the maintenance of individual differences in behavior and physiology is a fundamental goal in ecology and evolution. The pace‐of‐life syndrome hypothesis is often invoked to explain the maintenance of such within‐population variation. This hypothesis predicts that behavioral traits are part of a suite of correlated traits that collectively determine an individual's propensity to prioritize reproduction or survival. A key assumption of this hypothesis is that these traits are underpinned by genetic trade‐offs among life‐history traits: genetic variants that increase fertility, reproduction and growth might also reduce lifespan. We performed a systematic literature review and meta‐analysis to summarize the evidence for the existence of genetic trade‐offs between five key life‐history traits: survival, growth rate, body size, maturation rate, and fertility. Counter to our predictions, we found an overall positive genetic correlation between survival and other life‐history traits and no evidence for any genetic correlations between the non‐survival life‐history traits. This finding was generally consistent across pairs of life‐history traits, sexes, life stages, lab vs. field studies, and narrow‐ vs. broad‐sense correlation estimates. Our study highlights that genetic trade‐offs may not be as common, or at least not as easily quantifiable, in animals as often assumed. 

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5. Intervention Effects on Language in Children With Autism: A Project AIM Meta-Analysis

   https://doi.org/10.1044/2020_JSLHR-19-00167  

   Sandbank, Micheal; Bottema-Beutel, Kristen; Crowley, Shannon; Cassidy, Margaret; Feldman, Jacob I.; Canihuante, Marcos; Woynaroski, Tiffany (May 2020, Journal of Speech, Language, and Hearing Research)

   null (Ed.)

   Purpose This study synthesized effects of interventions on language outcomes of young children (ages 0–8 years) with autism and evaluated the extent to which summary effects varied by intervention, participant, and outcome characteristics. Method A subset of effect sizes gathered for a larger meta-analysis (the Autism Intervention Meta-analysis or Project AIM) examining the effects of interventions for young children with autism, which were specific to language outcomes, was analyzed. Robust variance estimation and metaregression were used to calculate summary and moderated effects while controlling for intercorrelation among outcomes within studies. Results A total of 221 outcomes were gathered from 60 studies. The summary effect of intervention on language outcomes was small but significant. Summary effects were larger for expressive and composite language outcomes compared to receptive language outcomes. Interventions implemented by clinicians, or by clinicians and caregivers together, had summary effects that were significantly larger than interventions implemented by caregivers alone. Participants' pretreatment language age equivalent scores positively and significantly moderated intervention effects, such that effects were significantly larger on average when samples of children had higher pretreatment language levels. Effects were not moderated by cumulative intervention intensity, intervention type, autism symptomatology, chronological age, or the proximity or boundedness of outcomes. Study quality concerns were apparent for a majority of included outcomes. Conclusions We found evidence that intervention can facilitate improvements in language outcomes for young children with autism. Effects were largest for expressive and composite language outcomes, for children with initially higher language abilities, and for interventions implemented by clinicians or by caregivers and clinicians combined. However, quality concerns of included studies and borderline significance of some results temper our conclusions regarding intervention effectiveness and corresponding moderators. 

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