Explore the vast range of titles available.

We present pytest-inline, the first inline testing framework for Python. We recently proposed inline tests to make it easier to test individual program statements. But, there is no framework-level support for developers to write inline tests in Python. To fill this gap, we design and implement pytest-inline as a plugin for pytest, the most popular Python testing framework. Using pytest-inline, a developer can write an inline test by assigning test inputs to variables in a target statement and specifying the expected test output. Then, pytest-inline runs each inline test and fails if the target statement's output does not match the expected output. In this paper, we describe our design of pytest-inline, the testing features that it provides, and the intended use cases. Our evaluation on inline tests that we wrote for 80 target statements from 31 open-source Python projects shows that using pytest-inline incurs negligible overhead, at 0.012x. pytest-inline is integrated into the pytest-dev organization, and a video demo is at https://www.youtube.com/watch?v=pZgiAxR_uJg.

For many pathologies associated with aging, female patients present with higher morbidity and more frequent adverse events from treatments compared to male patients. While preclinical models are the foundation of our mechanistic understanding of age-related diseases, the most common models fail to recapitulate archetypical female aging trajectories. For example, while over 70% of the top age-related diseases are influenced by the systemic effects of reproductive senescence, we found that preclinical studies that include menopausal phenotypes modeling those seen in humans make up <1% of published aging biology research. The long-term impacts of pregnancy, birthing and breastfeeding are also typically omitted from preclinical work. In this Perspective, we summarize limitations in the most commonly used aging models, and we provide recommendations for better incorporating menopause, pregnancy and other considerations of sex in vivo and in vitro. Lastly, we outline action items for aging biology researchers, journals, funding agencies and animal providers to address this gap.

Female individuals who are post-menopausal present with higher incidence of knee osteoarthritis (KOA) than male counterparts; however, the mechanisms underlying this disparity are unknown. The most commonly used preclinical models lack human-relevant menopausal phenotypes, which may contribute to our incomplete understanding of sex-specific differences in KOA pathogenesis. Here we chemically induced menopause in middle-aged (14–16 months) C57/BL6N female mice. When we mapped the trajectory of KOA over time, we found that menopause aggravated cartilage degeneration relative to non-menopause controls. Network medicine analyses revealed that loss of 17β-estradiol and progesterone with menopause enhanced susceptibility to senescence and extracellular matrix disassembly. In vivo, restoration of 17β-estradiol and progesterone in menopausal mice protected against cartilage degeneration compared to untreated menopausal controls. Accordingly, post-menopausal human chondrocytes displayed decreased markers of senescence and increased markers of chondrogenicity when cultured with 17β-estradiol and progesterone. These findings implicate menopause-associated senescence and extracellular matrix disassembly in the sex-specific pathogenesis of KOA. Knee osteoarthritis has a sex-specific phenotype with post-menopausal persons experiencing the highest incidence. Here the authors investigate the underlying mechanisms in a mouse model of menopause and find that the loss of 17β-estradiol and progesterone enhanced susceptibility to senescence, extracellular matrix disassembly and cartilage degradation.

The notion that data should be Findable, Accessible, Interoperable and Reusable, according to the FAIR Principles, has become a global norm for good data stewardship and a prerequisite for reproducibility. Nowadays, FAIR guides data policy actions and professional practices in the public and private sectors. Despite such global endorsements, however, the FAIR Principles are aspirational, remaining elusive at best, and intimidating at worst. To address the lack of practical guidance, and help with capability gaps, we developed the FAIR Cookbook, an open, online resource of hands-on recipes for “FAIR doers” in the Life Sciences. Created by researchers and data managers professionals in academia, (bio)pharmaceutical companies and information service industries, the FAIR Cookbook covers the key steps in a FAIRification journey, the levels and indicators of FAIRness, the maturity model, the technologies, the tools and the standards available, as well as the skills required, and the challenges to achieve and improve data FAIRness. Part of the ELIXIR ecosystem, and recommended by funders, the FAIR Cookbook is open to contributions of new recipes.

An experiment is proposed to distinguish between different laser-cluster atomistic models and their predictions. The induced transparency of rare-gas clusters, post-interaction with an extreme ultraviolet (XUV) pump-pulse, is predicted by using an atomistic hybrid quantum-classical molecular dynamics model. We find there is an intensity range for which an XUV probe-pulse has no lasting effect on the average charge state of a cluster after being saturated by an XUV pump-pulse: the cluster is transparent to the probe-pulse. Multiple complete experimental signals are calculated which include the effect of the pulse's spatial distribution as well as the cluster size distribution. The calculated experimental signals and trends are also accomplished with the addition of an ionization potential lowering model that results in effectively removing the induced transparency effect. Thus, the proposed experiment is expected to either find the new phenomenon of induced transparency in clusters or give strong evidence for the existence of the enhanced ionization phenomenon, ionization potential lowering, in nanoplasmas.

Ensuring that machine learning (ML) models are safe, effective, and equitable across all patient groups is essential for clinical decision-making and for preventing the reinforcement of existing health disparities. This review examines notions of fairness used in ML for health, including a review of why ML models can be unfair and how fairness has been quantified in a wide range of real-world examples. We provide an overview of commonly used fairness metrics and supplement our discussion with a case-study of an openly available electronic health record (EHR) dataset. We also discuss the outlook for future research, highlighting current challenges and opportunities in defining fairness in health.

Post-menopausal women present with the highest incidence and morbidity of knee osteoarthritis (KOA), but no disease-modifying therapies are available. This treatment gap may be driven by the absence of menopause in preclinical studies, as rodents do not naturally maintain a menopausal phenotype. Here, we employed a chemically-induced menopause model to map the trajectory of KOA at the tissue and proteome levels and test therapeutics in silico. Middle-aged female mice were randomized to sesame oil (non-menopause) or 4-vinycyclohexene diepoxide (menopause) injections. Following comprehensive validation of our model, knees were collected across perimenopause and menopause for histology, and cartilage samples were micro-dissected for mass spectrometry proteomics. Menopause mice displayed aggravated cartilage degeneration and synovitis relative to non-menopause mice. An unbiased pathway analysis revealed progesterone as a predominant driver of pathological signaling cascades within the cartilage proteome. Network medicine-based analyses suggested that menopause induction amplifies chondrocyte senescence, actin cytoskeleton-based stress, and extracellular matrix disassembly. We then used in silico drug testing to evaluate how restoration of sex hormones impacted the cartilage network. The greatest restoration was observed with combined estradiol/progesterone treatment (i.e., hormone therapy), although in silico treatment with a senolytic drug also partially recovered the cartilage proteome. Taken together, our findings using a translatable female aging model demonstrate that menopausal aging induces progressive cartilage degeneration and amplifies age-related synovitis. These changes may be driven by a previously unappreciated role of progesterone loss and menopause-induced cellular senescence. Lastly, in silico treatment suggests an estradiol/progesterone cocktail or senolytics may attenuate menopause-induced cartilage pathology.Competing Interest StatementThe authors have declared no competing interest.

While the importance of prior selection is well understood, establishing guidelines for selecting priors in hierarchical models has remained an active, and sometimes contentious, area of Bayesian methodology research. Choices of hyperparameters for individual families of priors are often discussed in the literature, but rarely are different families of priors compared under similar models and hyperparameters. Using simulated data, we evaluate the performance of inverse gamma and half-\\(t\\) priors for estimating the standard deviation of random effects in three hierarchical models: the 8-schools model, a random intercepts longitudinal model, and a simple multiple outcomes model. We compare the performance of the two prior families using a range of prior hyperparameters, some of which have been suggested in the literature, and others that allow for a direct comparison of pairs of half-\\(t\\) and inverse-gamma priors. Estimation of very small values of the random effect standard deviation led to convergence issues especially for the half-\\(t\\) priors. For most settings, we found that the posterior distribution of the standard deviation had smaller bias under half-\\(t\\) priors than under their inverse-gamma counterparts. Inverse gamma priors generally gave similar coverage but had smaller interval lengths than their half-\\(t\\) prior counterparts. Our results for these two prior families will inform prior specification for hierarchical models, allowing practitioners to better align their priors with their respective models and goals.