Arctic air pollution: Challenges and opportunities for the next decade

The Arctic is a sentinel of global change. This region is influenced by multiple physical and socio-economic drivers and feedbacks, impacting both the natural and human environment. Air pollution is one such driver that impacts Arctic climate change, ecosystems and health but significant uncertainties still surround quantification of these effects. Arctic air pollution includes harmful trace gases (e.g. tropospheric ozone) and particles (e.g. black carbon, sulphate) and toxic substances (e.g. polycyclic aromatic hydrocarbons) that can be transported to the Arctic from emission sources located far outside the region, or emitted within the Arctic from activities including shipping, power production, and other industrial activities. This paper qualitatively summarizes the complex science issues motivating the creation of a new international initiative, PACES (air Pollution in the Arctic: Climate, Environment and Societies). Approaches for coordinated, international and interdisciplinary research on this topic are described with the goal to improve predictive capability via new understanding about sources, processes, feedbacks and impacts of Arctic air pollution. Overarching research actions are outlined, in which we describe our recommendations for 1) the development of trans-disciplinary approaches combining social and economic research with investigation of the chemical and physical aspects of Arctic air pollution; 2) increasing the quality and quantity of observations in the Arctic using long-term monitoring and intensive field studies, both at the surface and throughout the troposphere; and 3) developing improved predictive capability across a range of spatial and temporal scales

Seasonality of aerosol optical properties in the Arctic

Given the sensitivity of the Arctic climate to short-lived climate forcers, long-term in situ surface measurements of aerosol parameters are useful in gaining insight into the magnitude and variability of these climate forcings. Seasonality of aerosol optical properties – including the aerosol light-scattering coefficient, absorption coefficient, single-scattering albedo, scattering Ångström exponent, and asymmetry parameter – are presented for six monitoring sites throughout the Arctic: Alert, Canada; Barrow, USA; Pallas, Finland; Summit, Greenland; Tiksi, Russia; and Zeppelin Mountain, Ny-Ålesund, Svalbard, Norway. Results show annual variability in all parameters, though the seasonality of each aerosol optical property varies from site to site. There is a large diversity in magnitude and variability of scattering coefficient at all sites, reflecting differences in aerosol source, transport, and removal at different locations throughout the Arctic. Of the Arctic sites, the highest annual mean scattering coefficient is measured at Tiksi (12.47&thinsp;Mm−1), and the lowest annual mean scattering coefficient is measured at Summit (1.74&thinsp;Mm−1). At most sites, aerosol absorption peaks in the winter and spring, and has a minimum throughout the Arctic in the summer, indicative of the Arctic haze phenomenon; however, nuanced variations in seasonalities suggest that this phenomenon is not identically observed in all regions of the Arctic. The highest annual mean absorption coefficient is measured at Pallas (0.48&thinsp;Mm−1), and Summit has the lowest annual mean absorption coefficient (0.12&thinsp;Mm−1). At the Arctic monitoring stations analyzed here, mean annual single-scattering albedo ranges from 0.909 (at Pallas) to 0.960 (at Barrow), the mean annual scattering Ångström exponent ranges from 1.04 (at Barrow) to 1.80 (at Summit), and the mean asymmetry parameter ranges from 0.57 (at Alert) to 0.75 (at Summit). Systematic variability of aerosol optical properties in the Arctic supports the notion that the sites presented here measure a variety of aerosol populations, which also experience different removal mechanisms. A robust conclusion from the seasonal cycles presented is that the Arctic cannot be treated as one common and uniform environment but rather is a region with ample spatiotemporal variability in aerosols. This notion is important in considering the design or aerosol monitoring networks in the region and is important for informing climate models to better represent short-lived aerosol climate forcers in order to yield more accurate climate predictions for the Arctic.</p

Retrospective model-based inference guides model-free credit assignment

An extensive reinforcement learning literature shows that organisms assign credit efficiently, even under conditions of state uncertainty. However, little is known about credit-assignment when state uncertainty is subsequently resolved. Here, we address this problem within the framework of an interaction between model-free (MF) and model-based (MB) control systems. We present and support experimentally a theory of MB retrospective-inference. Within this framework, a MB system resolves uncertainty that prevailed when actions were taken thus guiding an MF credit-assignment. Using a task in which there was initial uncertainty about the lotteries that were chosen, we found that when participants’ momentary uncertainty about which lottery had generated an outcome was resolved by provision of subsequent information, participants preferentially assigned credit within a MF system to the lottery they retrospectively inferred was responsible for this outcome. These findings extend our knowledge about the range of MB functions and the scope of system interactions

Effects Of Length, Complexity, And Grammatical Correctness On Stuttering In Spanish-Speaking Preschool Children

Purpose: To explore the effects of utterance length, syntactic complexity, and grammatical correctness on stuttering in the spontaneous speech of young, monolingual Spanish-speaking children. Method: Spontaneous speech samples of 11 monolingual Spanish-speaking children who stuttered, ages 35 to 70 months, were examined. Mean number of syllables, total number of clauses, utterance complexity (i.e., containing no clauses, simple clauses, or subordinate and/or conjoined clauses), and grammatical correctness (i.e., the presence or absence of morphological and syntactical errors) in stuttered and fluent utterances were compared. Results: Findings revealed that stuttered utterances in Spanish tended to be longer and more often grammatically incorrect, and contain more clauses, including more subordinate and/or conjoined clauses. However, when controlling for the interrelatedness of syllable number and clause number and complexity, only utterance length and grammatical incorrectness were significant predictors of stuttering in the spontaneous speech of these Spanish-speaking children. Use of complex utterances did not appear to contribute to the prediction of stuttering when controlling for utterance length. Conclusions: Results from the present study were consistent with many earlier reports of English-speaking children. Both length and grammatical factors appear to affect stuttering in Spanish-speaking children. Grammatical errors, however, served as the greatest predictor of stuttering.Communication Sciences and Disorder

Identifying component modules

A computer-based system for modelling component dependencies and identifying component modules is presented. A variation of the Dependency Structure Matrix (DSM) representation was used to model component dependencies. The system utilises a two-stage approach towards facilitating the identification of a hierarchical modular structure. The first stage calculates a value for a clustering criterion that may be used to group component dependencies together. A Genetic Algorithm is described to optimise the order of the components within the DSM with the focus of minimising the value of the clustering criterion to identify the most significant component groupings (modules) within the product structure. The second stage utilises a 'Module Strength Indicator' (MSI) function to determine a value representative of the degree of modularity of the component groupings. The application of this function to the DSM produces a 'Module Structure Matrix' (MSM) depicting the relative modularity of available component groupings within it. The approach enabled the identification of hierarchical modularity in the product structure without the requirement for any additional domain specific knowledge within the system. The system supports design by providing mechanisms to explicitly represent and utilise component and dependency knowledge to facilitate the nontrivial task of determining near-optimal component modules and representing product modularity

The Use of Technology to Support Precision Health in Nursing Science

PurposeThis article outlines how current nursing research can utilize technology to advance symptom and self‐management science for precision health and provides a roadmap for the development and use of technologies designed for this purpose.ApproachAt the 2018 annual conference of the National Institute of Nursing Research (NINR) Research Centers, nursing and interdisciplinary scientists discussed the use of technology to support precision health in nursing research projects and programs of study. Key themes derived from the presentations and discussion were summarized to create a proposed roadmap for advancement of technologies to support health and well‐being.ConclusionsTechnology to support precision health must be centered on the user and designed to be desirable, feasible, and viable. The proposed roadmap is composed of five iterative steps for the development, testing, and implementation of technology‐based/enhanced self‐management interventions. These steps are (a) contextual inquiry, focused on the relationships among humans, and the tools and equipment used in day‐to‐day life; (b) value specification, translating end‐user values into end‐user requirements; (c) design, verifying that the technology/device can be created and developing the prototype(s); (d) operationalization, testing the intervention in a real‐world setting; and (e) summative evaluation, collecting and analyzing viability metrics, including process data, to evaluate whether the technology and the intervention have the desired effect.Clinical RelevanceInterventions using technology are increasingly popular in precision health. Use of a standard multistep process for the development and testing of technology is essential.Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/151985/1/jnu12518.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/151985/2/jnu12518_am.pd

The historical Greenland Climate Network (GC-Net) curated and augmented level-1 dataset

The Greenland Climate Network (GC-Net) consists of 31 automatic weather stations (AWSs) at 30 sites across the Greenland Ice Sheet. The first site was initiated in 1990, and the project has operated almost continuously since 1995 under the leadership of the late Konrad Steffen. The GC-Net AWS measured air temperature, relative humidity, wind speed, atmospheric pressure, downward and reflected shortwave irradiance, net radiation, and ice and firn temperatures. The majority of the GC-Net sites were located in the ice sheet accumulation area (17 AWSs), while 11 AWSs were located in the ablation area, and two sites (three AWSs) were located close to the equilibrium line altitude. Additionally, three AWSs of similar design to the GC-Net AWS were installed by Konrad Steffen's team on the Larsen C ice shelf, Antarctica. After more than 3 decades of operation, the GC-Net AWSs are being decommissioned and replaced by new AWSs operated by the Geological Survey of Denmark and Greenland (GEUS). Therefore, making a reassessment of the historical GC-Net AWS data is necessary. We present a full reprocessing of the historical GC-Net AWS dataset with increased attention to the filtering of erroneous measurements, data correction and derivation of additional variables: continuous surface height, instrument heights, surface albedo, turbulent heat fluxes, and 10 m ice and firn temperatures. This new augmented GC-Net level-1 (L1) AWS dataset is now available at https://doi.org/10.22008/FK2/VVXGUT (Steffen et al., 2023) and will continue to be refined. The processing scripts, latest data and a data user forum are available at https://github.com/GEUS-Glaciology-and-Climate/GC-Net-level-1-data-processing (last access: 30 November 2023). In addition to the AWS data, a comprehensive compilation of valuable metadata is provided: maintenance reports, yearly pictures of the stations and the station positions through time. This unique dataset provides more than 320 station years of high-quality atmospheric data and is available following FAIR (findable, accessible, interoperable, reusable) data and code practices.</p

Precision health: A nursing perspective

Precision health refers to personalized healthcare based on a person's unique genetic, genomic, or omic composition within the context of lifestyle, social, economic, cultural and environmental influences to help individuals achieve well-being and optimal health. Precision health utilizes big data sets that combine omics (i.e. genomic sequence, protein, metabolite, and microbiome information) with clinical information and health outcomes to optimize disease diagnosis, treatment and prevention specific to each patient. Successful implementation of precision health requires interprofessional collaboration, community outreach efforts, and coordination of care, a mission that nurses are well-positioned to lead. Despite the surge of interest and attention to precision health, most nurses are not well-versed in precision health or its implications for the nursing profession. Based on a critical analysis of literature and expert opinions, this paper provides an overview of precision health and the importance of engaging the nursing profession for its implementation. Other topics reviewed in this paper include big data and omics, information science, integration of family health history in precision health, and nursing omics research in symptom science. The paper concludes with recommendations for nurse leaders in research, education, clinical practice, nursing administration and policy settings for which to develop strategic plans to implement precision health

Tryptophan degradation in women with breast cancer: a pilot study

<p>Abstract</p> <p>Background</p> <p>Altered tryptophan metabolism and indoleamine 2,3-dioxygenase activity are linked to cancer development and progression. In addition, these biological factors have been associated with the development and severity of neuropsychiatric syndromes, including major depressive disorder. However, this biological mechanism associated with both poor disease outcomes and adverse neuropsychiatric symptoms has received little attention in women with breast cancer. Therefore, a pilot study was undertaken to compare levels of tryptophan and other proteins involved in tryptophan degradation in women with breast cancer to women without cancer, and secondarily, to examine levels in women with breast caner over the course of chemotherapy.</p> <p>Findings</p> <p>Blood samples were collected from women with a recent diagnosis of breast cancer (<it>n </it>= 33) before their first cycle of chemotherapy and after their last cycle of chemotherapy. The comparison group (<it>n </it>= 24) provided a blood sample prior to breast biopsy. Plasma concentrations of tryptophan, kynurenine, and tyrosine were determined. The kynurenine to tryptophan ratio (KYN/TRP) was used to estimate indoleamine 2,3-dioxygenase activity. On average, the women with breast cancer had lower levels of tryptophan, elevated levels of kynurenine and tyrosine and an increased KYN/TRP ratio compared to women without breast cancer. There was a statistically significant difference between the two groups in the KYN/TRP ratio (<it>p </it>= 0.036), which remained elevated in women with breast cancer throughout the treatment trajectory.</p> <p>Conclusions</p> <p>The findings of this pilot study suggest that increased tryptophan degradation may occur in women with early-stage breast cancer. Given the multifactorial consequences of increased tryptophan degradation in cancer outcomes and neuropsychiatric symptom manifestation, this biological mechanism deserves broader attention in women with breast cancer.</p