Deconstructing a Syndrome: Genomic Insights into PCOS Causal Mechanisms and Classification

Polycystic ovary syndrome (PCOS) is among the most common disorders of reproductive-age women, affecting up to 15% worldwide, depending on the diagnostic criteria. PCOS is characterized by a constellation of interrelated reproductive abnormalities including disordered gonadotropin secretion, increased androgen production, chronic anovulation, and polycystic ovarian morphology. It is frequently associated with insulin resistance and obesity. These reproductive and metabolic derangements cause major morbidities across the lifespan, including anovulatory infertility and type 2 diabetes (T2D). Despite decades of investigative effort, the etiology of PCOS remains unknown. Familial clustering of PCOS cases has indicated a genetic contribution to PCOS. There are rare Mendelian forms of PCOS associated with extreme phenotypes, but PCOS typically follows a non-Mendelian pattern of inheritance consistent with a complex genetic architecture, analogous to T2D and obesity, that reflects the interaction of susceptibility genes and environmental factors. Genomic studies of PCOS have provided important insights into disease pathways and have indicated that current diagnostic criteria do not capture underlying differences in biology associated with different forms of PCOS. We provide a state-of-the-science review of genetic analyses of PCOS, including an overview of genomic methodologies aimed at a general audience of non-geneticists and clinicians. Applications in PCOS will be discussed, including strengths and limitations of each study. The contributions of environmental factors, including developmental origins, will be reviewed. Insights into the pathogenesis and genetic architecture of PCOS will be summarized. Future directions for PCOS genetic studies will be outlined.

Observed and Modeled Mountain Waves from the Surface to the Mesosphere Near the Drake Passage

Four state-of-the-science numerical weather prediction (NWP) models were used to perform mountain wave- (MW) resolving hind-casts over the Drake Passage of a 10-day period in 2010 with numerous observed MW cases. The Integrated Forecast System (IFS) and the Icosahedral Nonhydrostatic (ICON) model were run at Δx ≈ 9 and 13 km globally. TheWeather Research and Forecasting (WRF) model and the Met Office Unified Model (UM) were both configured with a Δx = 3 km regional domain. All domains had tops near 1 Pa (z ≈ 80 km). These deep domains allowed quantitative validation against Atmospheric InfraRed Sounder (AIRS) observations, accounting for observation time, viewing geometry, and radiative transfer. All models reproduced observed middle-atmosphere MWs with remarkable skill. Increased horizontal resolution improved validations. Still, all models underrepresented observed MW amplitudes, even after accounting for model effective resolution and instrument noise, suggesting even at Δx ≈ 3 km resolution, small-scale MWs are under-resolved and/or over-diffused. MWdrag parameterizations are still necessary in NWP models at current operational resolutions of Δx ≈ 10 km. Upper GW sponge layers in the operationally configured models significantly, artificially reduced MW amplitudes in the upper stratosphere and mesosphere. In the IFS, parameterized GW drags partly compensated this deficiency, but still, total drags were ≈ 6 time smaller than that resolved at Δx ≈ 3 km. Meridionally propagating MWs significantly enhance zonal drag over the Drake Passage. Interestingly, drag associated with meridional fluxes of zonal momentum (i.e. ) were important; not accounting for these terms results in a drag in the wrong direction at and below the polar night jet.

Air Pollution and Climate Change

Climate records kept worldwide clearly show that ongoing changes are happening in our eco-systems. Such climate changes include temperature, precipitation, or sea level, all of which are expected to keep changing well into the future, thereby affecting human health, the environment, and the economy. The natural causes by themselves are not able to describe these changes, so to understand these, scientists are using a combination of state-of-the-science measurements and models. Human activities are a major contributor due to the release of different air contaminants through various activities. Air pollution is one case-in-point, a human-made factor that contributes to climate change by affecting the amount of incoming sunlight that is either reflected or absorbed by the atmosphere. An overview of modeling techniques used to relate air quality and climate change is presented. The discussion includes the role of air pollution levels affecting the climate. Emerging topics such as black carbon (BC), fine particulate matters (PMs), role of cook stove, and risk assessment are also covered.

Digital Twins of Urban Air Quality: Opportunities and Challenges

The increasing amount of data collected about the environment brings tremendous potential to create digital systems that can predict the impact of intended and unintended changes. With growing interest in the construction of Digital Twins across multiple sectors, combined with rapid changes to where we spend our time and the nature of pollutants we are exposed to, we find ourselves at a crossroads of opportunity with regards to air quality mitigation in cities. With this in mind, we briefly discuss the interplay between available data and state of the science on air quality, infrastructure needs and areas of opportunities that should drive subsequent planning of the digital twin ecosystem and associated components. Data driven modeling and digital twins are promoted as the most efficient route to decision making in an evolving atmosphere. However, following the diverse data streams on which these frameworks are built, they must be supported by a diverse community. This is an opportunity to build a collaborative space to facilitate closer working between instrument manufacturers, data scientists, atmospheric scientists, and user groups including but not limited to regional and national policy makers.

Using Artificial Intelligence to Improve Pain Assessment and Pain Management: A Scoping Review

Approximate 50 million U.S. adults experience chronic pain. It is a widely held view that pain has been linked to sleep disturbance, mental problems, and reduced quality of life. Uncontrolled pain has led to increased healthcare utilization, hospitalization, emergency visits, and financial burden. Recognizing, assessing, understanding, and treating pain can improve outcomes of patients and healthcare use. A comprehensive synthesis of the current use of AI-based interventions in pain management and pain assessment and their outcomes will guide the development of future clinical trials. This review aims to investigate the state of the science of AI-based interventions designed to improve pain management and pain assessment for adult patients. The electronic databases Web of Science, CINAHL, PsycINFO, Cochrane CENTRAL, Scopus, IEEE Xplore, and ACM Digital Library were searched. The search identified 2131 studies, and 18 studies met the inclusion criteria. The Critical Appraisals Skills Programme was used to assess the quality. This review provides evidence that machine learning, deep learning, data mining, and natural language processing were used to improve efficient pain recognition and pain assessment (44%), analyze self-reporting pain data (6%), predict pain (6%), and help physicians and patients to more effectively manage with chronic pain (44%). Findings from this review suggest that using AI-based interventions to improve pain recognition, pain prediction, and pain self-management is effective; however, most studies are pilot study which raises concerns about the generalizability of findings. Future research should focus on examining AI-based approaches on a larger cohort and over a longer period of time.