Science advocacy in political rhetoric and actions.

'Science' is a proportionately small but recurring constituent in the rhetorical lexicon of political leaders. To evaluate the use of science-related content relative to other themes in political communications, we undertake a statistical analysis of keywords in U.S. Presidential State of the Union (SOTU) addresses and Presidential Budget Messages (PBM) from Truman (1947) to Trump (2020). Hierarchical clustering and correlation analyses reveal proximate affinities between 'science' and 'research', 'space', 'technology', 'education', and 'climate'. The keywords that are least correlated with 'science' relate to fiscal ('inflation', 'tax') and conflict-related themes ('security', 'war', 'terror'). The most ubiquitous and frequently used keywords are 'economy' and 'tax'. Science-related keywords are used in a positive (promotional) rhetorical context and thus their proportionality in SOTU and PBM corpora is used to define fields of science advocacy (public perception advocacy, funding advocacy, advocacy) for each president. Monte Carlo simulations and randomized sampling of three elements: language (relative frequency of usage of science-related keywords), funding (proposed funding and allocated discretionary funding of science agencies), and actions (e.g. expediency of science advisor appointments, (dis-) establishment of science agencies) are used to generate a science advocacy score (SAS) for each president. The SAS is compared with independent survey-based measures of political popularity. A myriad of political, contextual, and other factors may contribute to lexical choices, policy, and funding actions. Within this complex environment 'science' may have political currency under certain circumstances, particularly where public and political perceptions of the value of science to contribute to matters of priority align. Supplementary Information: The online version contains supplementary material available at 10.1007/s10669-022-09875-x.

Atmospheric modelling of grass pollen rupturing mechanisms for thunderstorm asthma prediction.

The world's most severe thunderstorm asthma event occurred in Melbourne, Australia on 21 November 2016, coinciding with the peak of the grass pollen season. The aetiological role of thunderstorms in these events is thought to cause pollen to rupture in high humidity conditions, releasing large numbers of sub-pollen particles (SPPs) with sizes very easily inhaled deep into the lungs. The humidity hypothesis was implemented into a three-dimensional atmospheric model and driven by inputs from three meteorological models. However, the mechanism could not explain how the Melbourne event occurred as relative humidity was very low throughout the atmosphere, and most available grass pollen remained within 40 m of the surface. Our tests showed humidity induced rupturing occurred frequently at other times and would likely lead to recurrent false alarms if used in a predictive capacity. We used the model to investigate a range of other possible pollen rupturing mechanisms which could have produced high concentrations of SPPs in the atmosphere during the storm. The mechanisms studied involve mechanical friction from wind gusts, electrical build up and discharge incurred during conditions of low relative humidity, and lightning strikes. Our results suggest that these mechanisms likely operated in tandem with one another, but the lightning method was the only mechanism to generate a pattern in SPPs following the path of the storm. If humidity induced rupturing cannot explain the 2016 Melbourne event, then new targeted laboratory studies of alternative pollen rupture mechanisms would be of considerable value to help constrain the parameterisation of the pollen rupturing process.

Air quality and health impact of 2019-20 Black Summer megafires and COVID-19 lockdown in Melbourne and Sydney, Australia.

Poor air quality is an emerging problem in Australia primarily due to ozone pollution events and lengthening and more severe wildfire seasons. A significant deterioration in air quality was experienced in Australia's most populous cities, Melbourne and Sydney, as a result of fires during the so-called Black Summer which ran from November 2019 through to February 2020. Following this period, social, mobility and economic restrictions to curb the spread of the COVID-19 pandemic were implemented in Australia. We quantify the air quality impact of these contrasting periods in the south-eastern states of Victoria and New South Wales (NSW) using a meteorological normalisation approach. A Random Forest (RF) machine learning algorithm was used to compute baseline time series' of nitrogen dioxide (NO2), ozone (O3), carbon monoxide CO and particulate matter with diameter < 2.5 µm (PM2.5), based on a 19 year, detrended training dataset. Across Victorian sites, large increases in CO (188%), PM2.5 (322%) and ozone (22%) were observed over the RF prediction in January 2020. In NSW, smaller pollutant increases above the RF prediction were seen (CO 58%, PM2.5 80%, ozone 19%). This can be partly explained by the RF predictions being high compared to the mean of previous months, due to high temperatures and strong wind speeds, highlighting the importance of meteorological normalisation in attributing pollution changes to specific events. From the daily observation-RF prediction differences we estimated 249.8 (95% CI: 156.6-343.) excess deaths and 3490.0 (95% CI 1325.9-5653.5) additional hospitalisations were likely as a result of PM2.5 and O3 exposure in Victoria and NSW. During April 2019, when COVID-19 restrictions were in place, on average NO2 decreased by 21.5 and 8% in Victoria and NSW respectively. O3 and PM2.5 remained effectively unchanged in Victoria on average but increased by 20 and 24% in NSW respectively, supporting the suggestion that community mobility reduced more in Victoria than NSW. Overall the air quality change during the COVID-19 lockdown had a negligible impact on the calculated health outcomes.

Using crowd-sourced allergic rhinitis symptom data to improve grass pollen forecasts and predict individual symptoms.

Seasonal allergic rhinitis (AR), also known as hay fever, is a common respiratory condition brought on by a range of environmental triggers. Previous work has characterised the relationships between community-level AR symptoms collected using mobile apps in two Australian cities, Canberra and Melbourne, and various environmental covariates including pollen. Here, we build on these relationships by assessing the skill of models that provide a next-day forecast of an individual's risk of developing AR and that nowcast ambient grass pollen concentrations using crowd-sourced AR symptoms as a predictor. Categorical grass pollen forecasts (low/moderate/high) were made based on binning mean daily symptom scores by corresponding categories. Models for an individual's risk were constructed by forward variable selection, considering environmental, demographic, behaviour and health-related inputs, with non-linear responses permitted. Proportional-odds logistic regression was then applied with the variables selected, modelling the symptom scores on their original five-point scale. AR symptom-based estimates of today's average grass pollen concentration were more accurate than those provided by two benchmark forecasting methods using various metrics for assessing accuracy. Predictions of an individual's next-day AR symptoms rated on a five-point scale were correct in 36% of cases and within one point on this scale in 82% of cases. Both outcomes were significantly better than chance. This large-scale AR symptoms measurement program shows that crowd-sourced symptom scores can be used to predict the daily average grass pollen concentration, as well as provide a personalised AR forecast.

Crowd-sourced allergic rhinitis symptom data: The influence of environmental and demographic factors.

Allergic Rhinitis (AR) affects over half a billion people worldwide with an estimated prevalence of 1 in 5 individuals in developed countries. Although ambient pollen exposure is a causal factor in AR, the symptom-exposure relationship is typically not studied in the broader community but in small, well-characterised cohorts drawn from clinical populations. To identify relationships between AR symptoms in the community and a range of environmental factors, we used a database containing over 96,000 symptom score reports collected over a 3-year period (2014-2016) through freely available smartphone apps released in two Australian cities, Melbourne and Canberra. Ambient pollen levels and symptom scores were strongly related, with grass pollen explaining most of the symptom variation. Other factors correlated with higher symptom scores included temperature (R > 0.73) and wind speed (R > 0.75). In general, worse symptom scores were reported by younger participants, women, and those who had taken medication for AR in the preceding 24 h. The strength of this relationship varied between the two cities. Smartphone-based symptom surveys offer a cost-effective means of studying real-world risk factors for AR in a broader 'extra-clinical' population.

Greenhouse Gas Concentration and Volcanic Eruptions Controlled the Variability of Terrestrial Carbon Uptake Over the Last Millennium.

The terrestrial net biome production (NBP) is considered as one of the major drivers of interannual variation in atmospheric CO2 levels. However, the determinants of variability in NBP under the background climate (i.e., preindustrial conditions) remain poorly understood, especially on decadal-to-centennial timescales. We analyzed 1,000-year simulations spanning 850-1,849 from the Community Earth System Model (CESM) and found that the variability in NBP and heterotrophic respiration (RH) were largely driven by fluctuations in the net primary production (NPP) and carbon turnover rates in response to climate variability. On interannual to multidecadal timescales, variability in NBP was dominated by variation in NPP, while variability in RH was driven by variation in turnover rates. However, on centennial timescales (100-1,000 years), the RH variability became more tightly coupled to that of NPP. The NBP variability on centennial timescales was low, due to the near cancellation of NPP and NPP-driven RH changes arising from climate internal variability and external forcings: preindustrial greenhouse gases, volcanic eruptions, land use changes, orbital change, and solar activity. Factorial experiments showed that globally on centennial timescales, the forcing of changes in greenhouse gas concentrations were the largest contributor (51%) to variations in both NPP and RH, followed by volcanic eruptions impacting NPP (25%) and RH (31%). Our analysis of the carbon-cycle suggests that geoengineering solutions by injection of stratospheric aerosols might be ineffective on longer timescales.

The Melbourne epidemic thunderstorm asthma event 2016: an investigation of environmental triggers, effect on health services, and patient risk factors.

BACKGROUND: A multidisciplinary collaboration investigated the world's largest, most catastrophic epidemic thunderstorm asthma event that took place in Melbourne, Australia, on Nov 21, 2016, to inform mechanisms and preventive strategies. METHODS: Meteorological and airborne pollen data, satellite-derived vegetation index, ambulance callouts, emergency department presentations, and data on hospital admissions for Nov 21, 2016, as well as leading up to and following the event were collected between Nov 21, 2016, and March 31, 2017, and analysed. We contacted patients who presented during the epidemic thunderstorm asthma event at eight metropolitan health services (each including up to three hospitals) via telephone questionnaire to determine patient characteristics, and investigated outcomes of intensive care unit (ICU) admissions. FINDINGS: Grass pollen concentrations on Nov 21, 2016, were extremely high (>100 grains/m3). At 1800 AEDT, a gust front crossed Melbourne, plunging temperatures 10°C, raising humidity above 70%, and concentrating particulate matter. Within 30 h, there were 3365 (672%) excess respiratory-related presentations to emergency departments, and 476 (992%) excess asthma-related admissions to hospital, especially individuals of Indian or Sri Lankan birth (10% vs 1%, p<0·0001) and south-east Asian birth (8% vs 1%, p<0·0001) compared with previous 3 years. Questionnaire data from 1435 (64%) of 2248 emergency department presentations showed a mean age of 32·0 years (SD 18·6), 56% of whom were male. Only 28% had current doctor-diagnosed asthma. 39% of the presentations were of Asian or Indian ethnicity (25% of the Melbourne population were of this ethnicity according to the 2016 census, relative risk [RR] 1·93, 95% CI 1·74-2·15, p <0·0001). Of ten individuals who died, six were Asian or Indian (RR 4·54, 95% CI 1·28-16·09; p=0·01). 35 individuals were admitted to an intensive care unit, all had asthma, 12 took inhaled preventers, and five died. INTERPRETATION: Convergent environmental factors triggered a thunderstorm asthma epidemic of unprecedented magnitude, tempo, and geographical range and severity on Nov 21, 2016, creating a new benchmark for emergency and health service escalation. Asian or Indian ethnicity and current doctor-diagnosed asthma portended life-threatening exacerbations such as those requiring admission to an ICU. Overall, the findings provide important public health lessons applicable to future event forecasting, health care response coordination, protection of at-risk populations, and medical management of epidemic thunderstorm asthma. FUNDING: None.

Seasonal asthma in Melbourne, Australia, and some observations on the occurrence of thunderstorm asthma and its predictability.

We examine the seasonality of asthma-related hospital admissions in Melbourne, Australia, in particular the contribution and predictability of episodic thunderstorm asthma. Using a time-series ecological approach based on asthma admissions to Melbourne metropolitan hospitals, we identified seasonal peaks in asthma admissions that were centred in late February, June and mid-November. These peaks were most likely due to the return to school, winter viral infections and seasonal allergies, respectively. We performed non-linear statistical regression to predict daily admission rates as functions of the seasonal cycle, weather conditions, reported thunderstorms, pollen counts and air quality. Important predictor variables were the seasonal cycle and mean relative humidity in the preceding two weeks, with higher humidity associated with higher asthma admissions. Although various attempts were made to model asthma admissions, none of the models explained substantially more variation above that associated with the annual cycle. We also identified a list of high asthma admissions days (HAADs). Most HAADs fell in the late-February return-to-school peak and the November allergy peak, with the latter containing the greatest number of daily admissions. Many HAADs in the spring allergy peak may represent episodes of thunderstorm asthma, as they were associated with rainfall, thunderstorms, high ambient grass pollen levels and high humidity, a finding that suggests thunderstorm asthma is a recurrent phenomenon in Melbourne that occurs roughly once per five years. The rarity of thunderstorm asthma events makes prediction challenging, underscoring the importance of maintaining high standards of asthma management, both for patients and health professionals, especially during late spring and early summer.

Dominant regions and drivers of the variability of the global land carbon sink across timescales.

Net biome productivity (NBP) dominates the observed large variation of atmospheric CO2 annual increase over the last five decades. However, the dominant regions controlling inter-annual to multi-decadal variability of global NBP are still controversial (semi-arid regions vs. temperate or tropical forests). By developing a theory for partitioning the variance of NBP into the contributions of net primary production (NPP) and heterotrophic respiration (Rh ) at different timescales, and using both observation-based atmospheric CO2 inversion product and the outputs of 10 process-based terrestrial ecosystem models forced by 110-year observational climate, we tried to reconcile the controversy by showing that semi-arid lands dominate the variability of global NBP at inter-annual (<10 years) and tropical forests dominate at multi-decadal scales (>30 years). Results further indicate that global NBP variability is dominated by the NPP component at inter-annual timescales, and is progressively controlled by Rh with increasing timescale. Multi-decadal NBP variations of tropical rainforests are modulated by the Pacific Decadal Oscillation (PDO) through its significant influences on both temperature and precipitation. This study calls for long-term observations for the decadal or longer fluctuations in carbon fluxes to gain insights on the future evolution of global NBP, particularly in the tropical forests that dominate the decadal variability of land carbon uptake and are more effective for climate mitigation.

A genome-wide association study of men with symptoms of testicular dysgenesis syndrome and its network biology interpretation.

BACKGROUND: Testicular dysgenesis syndrome (TDS) is a common disease that links testicular germ cell cancer, cryptorchidism and some cases of hypospadias and male infertility with impaired development of the testis. The incidence of these disorders has increased over the last few decades, and testicular cancer now affects 1% of the Danish and Norwegian male population. METHODS: To identify genetic variants that span the four TDS phenotypes, the authors performed a genome-wide association study (GWAS) using Affymetrix Human SNP Array 6.0 to screen 488 patients with symptoms of TDS and 439 selected controls with excellent reproductive health. Furthermore, they developed a novel integrative method that combines GWAS data with other TDS-relevant data types and identified additional TDS markers. The most significant findings were replicated in an independent cohort of 671 Nordic men. RESULTS: Markers located in the region of TGFBR3 and BMP7 showed association with all TDS phenotypes in both the discovery and replication cohorts. An immunohistochemistry investigation confirmed the presence of transforming growth factor ß receptor type III (TGFBR3) in peritubular and Leydig cells, in both fetal and adult testis. Single-nucleotide polymorphisms in the KITLG gene showed significant associations, but only with testicular cancer. CONCLUSIONS: The association of single-nucleotide polymorphisms in the TGFBR3 and BMP7 genes, which belong to the transforming growth factor ß signalling pathway, suggests a role for this pathway in the pathogenesis of TDS. Integrating data from multiple layers can highlight findings in GWAS that are biologically relevant despite having border significance at currently accepted statistical levels.

Is the Kaiser Permanente model superior in terms of clinical integration?: a comparative study of Kaiser Permanente, Northern California and the Danish healthcare system.

BACKGROUND: Integration of medical care across clinicians and settings could enhance the quality of care for patients. To date, there is limited data on the levels of integration in practice. Our objective was to compare primary care clinicians' perceptions of clinical integration and three sub-aspects in two healthcare systems: Kaiser Permanente, Northern California (KPNC) and the Danish healthcare system (DHS). Further, we examined the associations between specific organizational factors and clinical integration within each system. METHODS: Comparable questionnaires were sent to a random sample of primary care clinicians in KPNC (n = 1103) and general practitioners in DHS (n = 700). Data were analysed using multiple logistic regression models. RESULTS: More clinicians in KPNC perceived to be part of a clinical integrated environment than did general practitioners in the DHS (OR = 3.06, 95% CI: 2.28, 4.12). Further, more KPNC clinicians reported timeliness of information transfer (OR = 2.25, 95% CI: 1.62, 3.13), agreement on roles and responsibilities (OR = 1.79, 95% CI: 1.30, 2.47) and established coordination mechanisms in place to ensure effective handoffs (OR = 6.80, 95% CI: 4.60, 10.06). None of the considered organizational factors in the sub-country analysis explained a substantial proportion of the variation in clinical integration. CONCLUSIONS: More primary care clinicians in KPNC reported clinical integration than did general practitioners in the DHS. Focused measures of clinical integration are needed to develop the field of clinical integration and to create the scientific foundation to guide managers searching for evidence based approaches.

Estimating haplotype effects for survival data.

Genetic association studies often investigate the effect of haplotypes on an outcome of interest. Haplotypes are not observed directly, and this complicates the inclusion of such effects in survival models. We describe a new estimating equations approach for Cox's regression model to assess haplotype effects for survival data. These estimating equations are simple to implement and avoid the use of the EM algorithm, which may be slow in the context of the semiparametric Cox model with incomplete covariate information. These estimating equations also lead to easily computable, direct estimators of standard errors, and thus overcome some of the difficulty in obtaining variance estimators based on the EM algorithm in this setting. We also develop an easily implemented goodness-of-fit procedure for Cox's regression model including haplotype effects. Finally, we apply the procedures presented in this article to investigate possible haplotype effects of the PAF-receptor on cardiovascular events in patients with coronary artery disease, and compare our results to those based on the EM algorithm.

Microarray background correction: maximum likelihood estimation for the normal-exponential convolution.

Background correction is an important preprocessing step for microarray data that attempts to adjust the data for the ambient intensity surrounding each feature. The "normexp" method models the observed pixel intensities as the sum of 2 random variables, one normally distributed and the other exponentially distributed, representing background noise and signal, respectively. Using a saddle-point approximation, Ritchie and others (2007) found normexp to be the best background correction method for 2-color microarray data. This article develops the normexp method further by improving the estimation of the parameters. A complete mathematical development is given of the normexp model and the associated saddle-point approximation. Some subtle numerical programming issues are solved which caused the original normexp method to fail occasionally when applied to unusual data sets. A practical and reliable algorithm is developed for exact maximum likelihood estimation (MLE) using high-quality optimization software and using the saddle-point estimates as starting values. "MLE" is shown to outperform heuristic estimators proposed by other authors, both in terms of estimation accuracy and in terms of performance on real data. The saddle-point approximation is an adequate replacement in most practical situations. The performance of normexp for assessing differential expression is improved by adding a small offset to the corrected intensities.

Array-based gene discovery with three unrelated subjects shows SCARB2/LIMP-2 deficiency causes myoclonus epilepsy and glomerulosclerosis.

Action myoclonus-renal failure syndrome (AMRF) is an autosomal-recessive disorder with the remarkable combination of focal glomerulosclerosis, frequently with glomerular collapse, and progressive myoclonus epilepsy associated with storage material in the brain. Here, we employed a novel combination of molecular strategies to find the responsible gene and show its effects in an animal model. Utilizing only three unrelated affected individuals and their relatives, we used homozygosity mapping with single-nucleotide polymorphism chips to localize AMRF. We then used microarray-expression analysis to prioritize candidates prior to sequencing. The disorder was mapped to 4q13-21, and microarray-expression analysis identified SCARB2/Limp2, which encodes a lysosomal-membrane protein, as the likely candidate. Mutations in SCARB2/Limp2 were found in all three families used for mapping and subsequently confirmed in two other unrelated AMRF families. The mutations were associated with lack of SCARB2 protein. Reanalysis of an existing Limp2 knockout mouse showed intracellular inclusions in cerebral and cerebellar cortex, and the kidneys showed subtle glomerular changes. This study highlights that recessive genes can be identified with a very small number of subjects. The ancestral lysosomal-membrane protein SCARB2/LIMP-2 is responsible for AMRF. The heterogeneous pathology in the kidney and brain suggests that SCARB2/Limp2 has pleiotropic effects that may be relevant to understanding the pathogenesis of other forms of glomerulosclerosis or collapse and myoclonic epilepsies.

Probabilistic analysis of recessive mutagenesis screen strategies.

Random mutagenesis screens for recessive phenotypes require three generations of breeding, using either a backcross (BC) or intercross (IC) strategy. Hence, they are more costly and technically demanding than those for dominant phenotypes. Maximizing the return from these screens requires maximizing the number of mutations that are bred to homozyosity in the G(3) generation. Using a probabilistic approach, we compare different designs of screens for recessive phenotypes and the impact each one has on the number of mutations that can be effectively screened. We address the issue of BC versus IC strategies and consider genome-wide, region-specific screens and suppressor screens. We find that optimally designed BC and IC screens allow the screening of, on average, similar numbers of mutations but that interpedigree variation is more pronounced when the IC strategy is employed. By conducting a retrospective analysis of published mutagenesis screens, we show that, depending on the strategy, a threefold difference in the numbers of mutations screened per animal used could be expected. This method allows researchers to contrast, for a range of experimental designs, the cost per mutation screened and to maximize the number of mutations that one can expect to screen in a given experiment.