Classifying Self-Reported Rheumatoid Arthritis Flares Using Daily Patient-Generated Data From a Smartphone App: Exploratory Analysis Applying Machine Learning Approaches.

BACKGROUND: The ability to predict rheumatoid arthritis (RA) flares between clinic visits based on real-time, longitudinal patient-generated data could potentially allow for timely interventions to avoid disease worsening. OBJECTIVE: This exploratory study aims to investigate the feasibility of using machine learning methods to classify self-reported RA flares based on a small data set of daily symptom data collected on a smartphone app. METHODS: Daily symptoms and weekly flares reported on the Remote Monitoring of Rheumatoid Arthritis (REMORA) smartphone app from 20 patients with RA over 3 months were used. Predictors were several summary features of the daily symptom scores (eg, pain and fatigue) collected in the week leading up to the flare question. We fitted 3 binary classifiers: logistic regression with and without elastic net regularization, a random forest, and naive Bayes. Performance was evaluated according to the area under the curve (AUC) of the receiver operating characteristic curve. For the best-performing model, we considered sensitivity and specificity for different thresholds in order to illustrate different ways in which the predictive model could behave in a clinical setting. RESULTS: The data comprised an average of 60.6 daily reports and 10.5 weekly reports per participant. Participants reported a median of 2 (IQR 0.75-4.25) flares each over a median follow-up time of 81 (IQR 79-82) days. AUCs were broadly similar between models, but logistic regression with elastic net regularization had the highest AUC of 0.82. At a cutoff requiring specificity to be 0.80, the corresponding sensitivity to detect flares was 0.60 for this model. The positive predictive value (PPV) in this population was 53%, and the negative predictive value (NPV) was 85%. Given the prevalence of flares, the best PPV achieved meant only around 2 of every 3 positive predictions were correct (PPV 0.65). By prioritizing a higher NPV, the model correctly predicted over 9 in every 10 non-flare weeks, but the accuracy of predicted flares fell to only 1 in 2 being correct (NPV and PPV of 0.92 and 0.51, respectively). CONCLUSIONS: Predicting self-reported flares based on daily symptom scorings in the preceding week using machine learning methods was feasible. The observed predictive accuracy might improve as we obtain more data, and these exploratory results need to be validated in an external cohort. In the future, analysis of frequently collected patient-generated data may allow us to predict flares before they unfold, opening opportunities for just-in-time adaptative interventions. Depending on the nature and implication of an intervention, different cutoff values for an intervention decision need to be considered, as well as the level of predictive certainty required.

Protracted metallogenic and magmatic evolution of the Kirazli epithermal Au-Ag and porphyry Cu deposits, Biga Peninsula, NW Turkey: evidence from zircon U-Pb, muscovite 40Ar/39Ar, and molybdenite Re-Os geochronology.

The Kirazli deposit is located at the center of the Biga Peninsula metallogenic province, in a geological setting characterized by an extensional tectonic environment. A NNW-SSE trending high-sulfidation (HS) orebody with a total reserve of 33.86 Mt @ 0.69 g/t Au and 9.42 g/t Ag lies beneath the Kirazli Main zone. A porphyry Cu orebody hosted by Eocene intrusive and volcanic rocks has been intersected by drilling within its vicinity. The HS epithermal deposit is hosted by a partly silicified and brecciated Oligocene volcanic and volcaniclastic sequence consisting mainly of basaltic andesite lava flow and lithic/crystal tuff. Lithogeochemistry and zircon U-Pb radiometric ages allow us to distinguish three distinct high-K calc-alkaline magmatic events at ca. 41, 38, and 32 Ma, sourced by metasomatized mantle melts, which have interacted with the crust during their ascent. Porphyry Cu mineralization took place at 36.7 ± 0.4 Ma (muscovite 40Ar/39Ar age) with subsequent re-opening and base metal deposition. Crosscutting quartz-pyrite-molybdenite veins were emplaced at 33.6 ± 0.2 Ma (molybdenite Re-Os age), and followed by the HS epithermal Au-Ag event at ca. 31 Ma, based on a previous study. Our radiometric data indicate that the Kirazli deposit has recorded a long-lasting Cenozoic magmatic and metallogenic evolution during about 10 Myr. Our study demonstrates that successive, independent, and overprinting, but genetically unrelated, HS epithermal precious metal, hydrothermal Mo, base metal, and porphyry Cu systems have been active at the same location during protracted extensional tectonics of the Biga Peninsula. Supplementary Information: The online version contains supplementary material available at 10.1007/s00126-023-01235-2.

Feasibility and acceptability to use a smartphone-based manikin for daily longitudinal self-reporting of chronic pain.

Background: As management of chronic pain continues to be suboptimal, there is a need for tools that support frequent, longitudinal pain self-reporting to improve our understanding of pain. This study aimed to assess the feasibility and acceptability of daily pain self-reporting using a smartphone-based pain manikin. Methods: For this prospective feasibility study, we recruited adults with lived experience of painful musculoskeletal condition. They were asked to complete daily pain self-reports via an app for 30 days. We assessed feasibility by calculating pain report completion levels, and investigated differences in completion levels between subgroups. We assessed acceptability via an end-of-study questionnaire, which we analysed descriptively. Results: Of the 104 participants, the majority were female (n = 87; 84%), aged 45-64 (n = 59; 57%), and of white ethnic background (n = 89; 86%). The mean completion levels was 21 (± 7.7) pain self-reports. People who were not working (odds ratio (OR) = 1.84; 95% confidence interval (CI), 1.52-2.23) were more likely, and people living in less deprived areas (OR = 0.77; 95% CI, 0.62-0.97) and of non-white ethnicity (OR = 0.45; 95% CI, 0.36-0.57) were less likely to complete pain self-reports than their employed, more deprived and white counterparts, respectively. Of the 96 participants completing the end-of-study questionnaire, almost all participants agreed that it was easy to complete a pain drawing (n = 89; 93%). Conclusion: It is feasible and acceptable to self-report pain using a smartphone-based manikin over a month. For its wider adoption for pain self-reporting, the feasibility and acceptability should be further explored among people with diverse socio-economic and ethnic backgrounds.

Hospital admissions linked to SARS-CoV-2 infection in children and adolescents: cohort study of 3.2 million first ascertained infections in England.

OBJECTIVE: To describe hospital admissions associated with SARS-CoV-2 infection in children and adolescents. DESIGN: Cohort study of 3.2 million first ascertained SARS-CoV-2 infections using electronic health care record data. SETTING: England, July 2020 to February 2022. PARTICIPANTS: About 12 million children and adolescents (age <18 years) who were resident in England. MAIN OUTCOME MEASURES: Ascertainment of a first SARS-CoV-2 associated hospital admissions: due to SARS-CoV-2, with SARS-CoV-2 as a contributory factor, incidental to SARS-CoV-2 infection, and hospital acquired SARS-CoV-2. RESULTS: 3 226 535 children and adolescents had a recorded first SARS-CoV-2 infection during the observation period, and 29 230 (0.9%) infections involved a SARS-CoV-2 associated hospital admission. The median length of stay was 2 (interquartile range 1-4) days) and 1710 of 29 230 (5.9%) SARS-CoV-2 associated admissions involved paediatric critical care. 70 deaths occurred in which covid-19 or paediatric inflammatory multisystem syndrome was listed as a cause, of which 55 (78.6%) were in participants with a SARS-CoV-2 associated hospital admission. SARS-CoV-2 was the cause or a contributory factor in 21 000 of 29 230 (71.8%) participants who were admitted to hospital and only 380 (1.3%) participants acquired infection as an inpatient and 7855 (26.9%) participants were admitted with incidental SARS-CoV-2 infection. Boys, younger children (<5 years), and those from ethnic minority groups or areas of high deprivation were more likely to be admitted to hospital (all P<0.001). The covid-19 vaccination programme in England has identified certain conditions as representing a higher risk of admission to hospital with SARS-CoV-2: 11 085 (37.9%) of participants admitted to hospital had evidence of such a condition, and a further 4765 (16.3%) of participants admitted to hospital had a medical or developmental health condition not included in the vaccination programme's list. CONCLUSIONS: Most SARS-CoV-2 associated hospital admissions in children and adolescents in England were due to SARS-CoV-2 or SARS-CoV-2 was a contributory factor. These results should inform future public health initiatives and research.

Day-to-day variability of knee pain and the relationship with physical activity in people with knee osteoarthritis: an observational, feasibility study using consumer smartwatches.

OBJECTIVE: To assess the feasibility of using smartwatches in people with knee osteoarthritis (OA) to determine the day-to-day variability of pain and the relationship between daily pain and step count. DESIGN: Observational, feasibility study. SETTING: In July 2017, the study was advertised in newspapers, magazines and, on social media. Participants had to be living/willing to travel to Manchester. Recruitment was in September 2017 and data collection was completed in January 2018. PARTICIPANTS: 26 participants aged>50 years with self-diagnosed symptomatic knee OA were recruited. OUTCOME MEASURES: Participants were provided with a consumer cellular smartwatch with a bespoke app that triggered a series of daily questions including two times per day questions about level of knee pain and one time per month question from the pain subscale of the Knee Injury and Osteoarthritis Outcome Score (KOOS) questionnaire. The smartwatch also recorded daily step counts. RESULTS: Of the 25 participants, 13 were men and their mean age was 65 years (standard deviation (SD) 8 years). The smartwatch app was successful in simultaneously assessing and recording data on knee pain and step count in real time. Knee pain was categorised into sustained high/low or fluctuating levels, but there was considerable day-to-day variation within these categories. Levels of knee pain in general correlated with pain assessed by KOOS. Those with sustained high/low levels of pain had a similar daily step count average (mean 3754 (SD 2524)/4307 (SD 2992)), but those with fluctuating pain had much lower step count levels (mean 2064 (SD 1716)). CONCLUSIONS: Smartwatches can be used to assess pain and physical activity in knee OA. Larger studies may help inform a better understanding of causal links between physical activity patterns and pain. In time, this could inform development of personalised physical activity recommendations for people with knee OA.

Examining the variability of multiple daily symptoms over time among individuals with multiple long-term conditions (MLTC-M/multimorbidity): An exploratory analysis of a longitudinal smartwatch feasibility study.

Introduction: People living with multiple long-term conditions (MLTC-M) (multimorbidity) experience a range of inter-related symptoms. These symptoms can be tracked longitudinally using consumer technology, such as smartphones and wearable devices, and then summarised to provide useful clinical insight. Aim: We aimed to perform an exploratory analysis to summarise the extent and trajectory of multiple symptom ratings tracked via a smartwatch, and to investigate the relationship between these symptom ratings and demographic factors in people living with MLTC-M in a feasibility study. Methods: 'Watch Your Steps' was a prospective observational feasibility study, administering multiple questions per day over a 90 day period. Adults with more than one clinician-diagnosed long-term condition rated seven core symptoms each day, plus up to eight additional symptoms personalised to their LTCs per day. Symptom ratings were summarised over the study period at the individual and group level. Symptom ratings were also plotted to describe day-to-day symptom trajectories for individuals. Results: Fifty two participants submitted symptom ratings. Half were male and the majority had LTCs affecting three or more disease areas (N = 33, 64%). The symptom rated as most problematic was fatigue. Patients with increased comorbidity or female sex seemed to be associated with worse experiences of fatigue. Fatigue ratings were strongly correlated with pain and level of dysfunction. Conclusion: In this study we have shown that it is possible to collect and descriptively analyse self reported symptom data in people living with MLTC-M, collected multiple times per day on a smartwatch, to gain insights that might support future clinical care and research.

"Take up to eight tablets per day": Incorporating free-text medication instructions into a transparent and reproducible process for preparing drug exposure data for pharmacoepidemiology.

PURPOSE: Routinely collected prescription data provides drug exposure information for pharmacoepidemiology, informing start/stop dates and dosage. Prescribing information includes structured data and unstructured free-text instructions, which can include inherent variability, such as "one to two tablets up to four times a day". Preparing drug exposure data from raw prescriptions to a research ready dataset is rarely fully reported, yet assumptions have considerable implications for pharmacoepidemiology. This may have bigger consequences for "pro re nata" (PRN) drugs. Our aim was, using a worked example of opioids and fracture risk, to examine the impact of incorporating narrative prescribing instructions and subsequent drug preparation assumptions on adverse event rates. METHODS: R-packages for extracting free-text medication prescription instructions in a structured form (doseminer) and an algorithm for transparently processing drug exposure information (drugprepr) were developed. Clinical Practice Research Datalink GOLD was used to define a cohort of adult new opioid users without prior cancer. A retrospective cohort study was performed using data between January 1, 2017 and July 31, 2018. We tested the impact of varying drug preparation assumptions by estimating the risk of opioids on fracture risk using Cox proportional hazards models. RESULTS: During the study window, 60 394 patients were identified with 190 754 opioid prescriptions. Free-text prescribing instruction variability, where there was flexibility in the number of tablets to be administered, was present in 42% prescriptions. Variations in the decisions made during preparing raw data for analysis led to marked differences impacting the event number (n = 303-415) and person years of drug exposure (5619-9832). The distribution of hazard ratios as a function of the decisions ranged from 2.71 (95% CI: 2.31, 3.18) to 3.24 (2.76, 3.82). CONCLUSIONS: Assumptions made during the drug preparation process, especially for those with variability in prescription instructions, can impact results of subsequent risk estimates. The developed R packages can improve transparency related to drug preparation assumptions, in line with best practice advocated by international pharmacoepidemiology guidelines.

A rapid change in magma plumbing taps porphyry copper deposit-forming magmas.

Porphyry-type deposits are a vital source of green technology metals such as copper and molybdenum. They typically form in subduction-related settings from large, long-lived magmatic systems. The most widely accepted model for their formation requires that mantle-derived magmas undergo an increase in volatiles and ore-forming constituents in mid- to lower crustal reservoirs over millions of years, however, this is mostly based on observations from shallow, sporadically exposed parts of porphyry systems. To examine this paradigm, we have evaluated the timeframe and geochemical signatures of magmatism in a ~ 8 km palaeodepth cross-section through plutonic and volcanic rocks of the classic Yerington magmatic system, Nevada. We show that the magmas in the upper parts of the system (< 8 km) underwent a major and rapid change in chemistry over a period of < 200 kyrs that is coincident with the initiation of ore formation. We attribute this change to a shift from extraction of quartz monzodiorite and quartz monzonite magmas evolving in mid-crustal reservoirs, and that had relatively poor ore-forming potential, to extraction of volatile-rich granitic magmas from greater (~ 30 km) depths. As the granites crystallised, late stage melts were intruded through the carapace as aplite dykes which contain traceable expressions of the porphyry deposit-forming fluids. The rapid nature of the shift in ore-forming potential narrows the temporal-geochemical footprint of magmas associated with porphyry mineralisation and provides new constraints for exploration models.

Does body mass index mediate the relationship between socioeconomic position and incident osteoarthritis?

OBJECTIVES: To investigate associations of socioeconomic position (SEP) and obesity with incident osteoarthritis (OA), and to examine whether body mass index (BMI) mediates the association between SEP and incident OA. METHODS: Data came from the English Longitudinal Study of Ageing, a population-based cohort study of adults aged ≥50 years. The sample population included 9,281 people. Cox regression analyses were performed to investigate the associations between SEP (measured by education, occupation, income, wealth and deprivation) and obesity (BMI ≥30 kg/m2) at baseline and self-reported incident OA. The mediating effect of BMI on the relationship between SEP and incident OA were estimated using Structural Equation Models. RESULTS: After a mean follow-up time of 7.8 years, 2369 participants developed OA. Number of person-years included in the analysis was 65,456. Lower SEP was associated with higher rates of OA (for example, hazard ratio (HR) lowest vs highest education category 1.52 (95% confidence interval (CI) 1.30, 1.79)). Obesity compared with non-obesity was associated with increased rates of incident OA (HR 1.37 (95% CI 1.23, 1.52)). BMI mediated the relationship between a lower SEP and OA (ß = 0.005, p < 0.001) and the direct effect was not significant (ß = 0.004, p = 0.212). CONCLUSIONS: Strategies to reduce social inequalities and obesity prevalence may help to reduce OA risk.

Using patient-reported data from a smartphone app to capture and characterize real-time patient-reported flares in rheumatoid arthritis.

Objective: We aimed to explore the frequency of self-reported flares and their association with preceding symptoms collected through a smartphone app by people with RA. Methods: We used data from the Remote Monitoring of RA study, in which patients tracked their daily symptoms and weekly flares on an app. We summarized the number of self-reported flare weeks. For each week preceding a flare question, we calculated three summary features for daily symptoms: mean, variability and slope. Mixed effects logistic regression models quantified associations between flare weeks and symptom summary features. Pain was used as an example symptom for multivariate modelling. Results: Twenty patients tracked their symptoms for a median of 81 days (interquartile range 80, 82). Fifteen of 20 participants reported at least one flare week, adding up to 54 flare weeks out of 198 participant weeks in total. Univariate mixed effects models showed that higher mean and steeper upward slopes in symptom scores in the week preceding the flare increased the likelihood of flare occurrence, but the association with variability was less strong. Multivariate modelling showed that for pain, mean scores and variability were associated with higher odds of flare, with odds ratios 1.83 (95% CI, 1.15, 2.97) and 3.12 (95% CI, 1.07, 9.13), respectively. Conclusion: Our study suggests that patient-reported flares are common and are associated with higher daily RA symptom scores in the preceding week. Enabling patients to collect daily symptom data on their smartphones might, ultimately, facilitate prediction and more timely management of imminent flares.

Predicting long-term trends in inflammatory neuropathy outcome measures using latent class modelling.

Immunoglobulin (Ig) is used to treat chronic inflammatory demyelinating polyradiculoneuropathy (CIDP) and multifocal motor neuropathy with conduction block (MMNCB). Regular infusions may be used for symptom control. Disease activity is monitored with clinical outcome measurements. We examined outcome measure variation during clinically stable periods in Ig-treated CIDP and MMNCB patients. We explored utility of serial outcome measurement in long-term outcome prediction. Retrospective longitudinal analysis of a single neuroscience centre's Ig-treated CIDP and MMNCB patients, 2009-2020, was performed. Mean and percentage change for grip strength, Rasch-built overall disability scales (RODS) and MRC sum scores (MRC-SS) during periods of clinical stability were compared to score-specific minimal clinically important differences (MCID). Latent class mixed modelling (LCMM) was used to identify longitudinal trends and factors influencing long-term outcome. We identified 85 CIDP and 23 MMNCB patients (1423 datapoints; 5635 treatment-months). Group-averaged outcome measures varied little over time. Intra-individual variation exceeded MCID for RODS in 44.2% CIDP and 16.7% MMNCB datapoints, grip strength in 10.6% (CIDP) and 8.8%/27.2% (MMNCB right/left hand) and MRC-SS in 43.5% (CIDP) and 20% (MMNCB). Multivariate LCMM identified subclinical trends towards improvement (32 patients) and deterioration (73 patients) in both cohorts. At baseline, CIDP 'deteriorators' were older than 'improvers' (66.2 vs 57 years, P = .025). No other individual factors predicted categorisation. The best model for 'deteriorator' identification was contiguous sub-MCID decline in more than one outcome measure (CIDP: sensitivity 74%, specificity 59%; MMNCB: sensitivity 73%, specificity 88%). Outcome measure interpretation determines therapeutic decision-making in Ig-dependent neuropathy patients, but intra-individual variation is common, often exceeding MCID. Here we show sub-MCID contiguous changes in more than one outcome measurement are a better predictor of long-term outcome.

Engagement with consumer smartwatches for tracking symptoms of individuals living with multiple long-term conditions (multimorbidity): A longitudinal observational study.

INTRODUCTION: People living with multiple long-term conditions (multimorbidity) (MLTC-M) experience an accumulating combination of different symptoms. It has been suggested that these symptoms can be tracked longitudinally using consumer technology, such as smartphones and wearable devices. AIM: The aim of this study was to investigate longitudinal user engagement with a smartwatch application, collecting survey questions and active tasks over 90 days, in people living with MLTC-M. METHODS: 'Watch Your Steps' was a prospective observational study, administering multiple questions and active tasks over 90 days. Adults with more than one clinician-diagnosed long-term conditions were loaned Fossil® Sport smartwatches, pre-loaded with the study app. Around 20 questions were prompted per day.Daily completion rates were calculated to describe engagement patterns over time, and to explore how these varied by patient characteristics and question type. RESULTS: Fifty three people with MLTC-M took part in the study. Around half were male ( = 26; 49%) and the majority had a white ethnic background (n = 45; 85%). About a third of participants engaged with the smartwatch app nearly every day. The overall completion rate of symptom questions was 45% inter-quartile range (IQR 23-67%) across all study participants. Older patients and those with greater MLTC-M were more engaged, although engagement was not significantly different between genders. CONCLUSION: It was feasible for people living with MLTC-M to report multiple symptoms per day over 3 months. User engagement appeared as good as other mobile health studies that recruited people with single health conditions, despite the higher daily data entry burden.

Anthropogenic Osmium in Macroalgae from Tokyo Bay Reveals Widespread Contamination from Municipal Solid Waste.

Human activity is influencing the global osmium cycle, driving the Os isotopic composition (187Os/188Os) of the hydrosphere and associated sedimentary material to lower values. Here, we present the Re and Os abundance and isotope systematics of macroalgae, a proxy for seawater, from Tokyo Bay to elucidate the potential sources of anthropogenic Os to the Pacific Ocean. Macroalgae from the Uraga Channel, which connects Tokyo Bay to the Pacific Ocean, record relatively low Os abundances (∼10.1 pg/g) and an 187Os/188Os of ∼0.9, indicative of surface ocean seawater. Contrastingly, macroalgae within the bay closest to central Tokyo record the highest Os abundances (∼22.8 pg/g) and lowest 187Os/188Os values (∼0.47), suggesting contamination from human activity. To determine the source of anthropogenic Os, we have developed the first Os emission inventory, based on the East Asian Air Pollutant Emission Grid database (EAGrid2010). The close relationship (R2 = 0.67 and p-value = <0.05) between Os inventories and macroalgal data suggests that municipal solid waste incinerators (MSWIs) are the dominant source of Os to Tokyo Bay. Projections for Japan estimate that 26-18+38 ng Os/m2/yr is released from MSWI smokestacks, leading to a concentration in precipitation of 26-18+38 fg/g, identifying MSWIs as a major contributor of anthropogenic Os to the hydrological cycle.

Biomass-Derived Provenance Dominates Glacial Surface Organic Carbon in the Western Himalaya.

The origin, transport pathway, and spatial variability of total organic carbon (OC) in the western Himalayan glaciers are poorly understood compared to those of black carbon (BC) and dust, but it is critically important to evaluate the climatic role of OC in the region. By applying the distribution of OC activation energy; 14C activity; and radiogenic isotopes of 208Pb/204Pb, 207Pb/204Pb, and 206Pb/204Pb in glacial debris and atmospheric particulate matter (PM10 size fraction), we demonstrate that 98.3 ± 1.6 and 1.7 ± 1.6% of OC in western Himalayan glaciers are derived from biomass and petrogenic sources, respectively. The δ13C and N/C composition indicates that the biomass is a complex mixture of C3 vegetation and autochthonous photoautotrophic input modified by heterotrophic microbial activity. The data set reveals that the studied western Himalayan glacier has negligible contributions from fossil-fuel-derived particles, which contrasts to the central and eastern Himalayan glaciers that have significant contributions from fossil fuel sources. We show that this spatial variability of OC sources relates to regional differences in air mass transport pathways and precipitation regimes over the Himalaya. Moreover, our observation suggests that biomass-derived carbon could be the only primary driver of carbon-induced glacier melting in the western Himalaya.

Tracing the natural and anthropogenic influence on the trace elemental chemistry of estuarine macroalgae and the implications for human consumption.

Macroalgae (seaweed) has been shown to be an effective environmental indicator. We investigate the trace element chemistry of macroalgae samples from locations along the Firth of Forth and Forth Estuary in Scotland. The overall trend in elemental abundance (Os ≪ Re < Ag < U < Cd < Co < Ni < Pb < Cu < As < Zn ≪ I), and changes along the estuary (seawards: increase As, I, Cd, U, Re, Os; decrease Pb, Cu; mid-estuary peak Zn; based on certain species), are controlled by a number of factors, including: salinity, mixing and macroalgal species differences. Within the same macroalgal species, some elemental abundances (As, I, Pb, Cu, Cd and U) are affected by mixing between freshwater riverine and North Sea marine saltwater. Additional mixing of natural and anthropogenic inputs from the surrounding geology and industry are also observed, affecting Zn, Ni, Co, Re and Os. Macroalgae is also an increasingly popular food, with some species harvested in the Firth of Forth. Iodine (67-5061 ppm), lead (0.047-4.1 ppm) and cadmium (0.006-0.93 ppm) macroalgal abundances are at safe levels for human consumption (WHO limits). However, many samples exceed the American (3 ppm) and Australian (1 ppm) limits for inorganic arsenic in macroalgae, with values ranging 0-67 ppm. In most of the samples, soaking and cooking the macroalgae reduced the inorganic arsenic content to within the American and Australian limits. However, this has further implications if the macroalgae is used to cook soups (e.g., Dashi), as the leached elements become a significant component of the soup.

Remotely constraining the temporal evolution of offshore oil systems.

An understanding of the temporal evolution of a petroleum system is fundamental to interpreting where hydrocarbons may be trapped in the subsurface. However, traditional exploration methods provide few absolute constraints on the timing of petroleum generation. Here we show that 187Re/187Os geochronology may be applied to natural crude oil seepage to determine when petroleum generation occurred in offshore sedimentary basins. Using asphaltites collected from the South Australian coastline, our determined Re-Os age (68 ± 15 million years ago) is consistent with their derivation from a Late Cretaceous source rock in the nearby Bight Basin, an interpretation similarly favoured by source-specific biomarker constraints. Furthermore, the calculated initial 187Os/188Os composition of the asphaltites, a value inherited from the source rock at the time of oil generation, suggests that the source rock represents the later stage of Oceanic Anoxic Event 2. Our results demonstrate a new approach to identifying the origin of crude oils encountered in coastal environments by providing direct constraints on the timing of petroleum generation and potential source rock intervals in poorly characterised offshore sedimentary basins prior to exploratory drilling.

Monte Carlo sampling for error propagation in linear regression and applications in isochron geochronology.

Geochronology is essential for understanding Earth's history. The availability of precise and accurate isotopic data is increasing; hence it is crucial to develop transparent and accessible data reduction techniques and tools to transform raw mass spectrometry data into robust chronological data. Here we present a Monte Carlo sampling approach to fully propagate uncertainties from linear regressions for isochron dating. Our new approach makes no prior assumption about the causes of variability in the derived chronological results and propagates uncertainties from both experimental measurements (analytical uncertainties) and underlying assumptions (model uncertainties) into the final age determination. Using synthetic examples, we find that although the estimates of the slope and y-intercept (hence age and initial isotopic ratios) are comparable between the Monte Carlo method and the benchmark "Isoplot" algorithm, uncertainties from the later could be underestimated by up to 60%, which are likely due to an incomplete propagation of model uncertainties. An additional advantage of the new method is its ability to integrate with geological information to yield refined chronological constraints. The new method presented here is specifically designed to fully propagate errors in geochronological applications involves linear regressions such as Rb-Sr, Sm-Nd, Re-Os, Pt-Os, Lu-Hf, U-Pb (with discordant points), Pb-Pb and Ar-Ar.

Middle Eocene greenhouse warming facilitated by diminished weathering feedback.

The Middle Eocene Climatic Optimum (MECO) represents a ~500-kyr period of global warming ~40 million years ago and is associated with a rise in atmospheric CO2 concentrations, but the cause of this CO2 rise remains enigmatic. Here we show, based on osmium isotope ratios (187Os/188Os) of marine sediments and published records of the carbonate compensation depth (CCD), that the continental silicate weathering response to the inferred CO2 rise and warming was strongly diminished during the MECO-in contrast to expectations from the silicate weathering thermostat hypothesis. We surmise that global early and middle Eocene warmth gradually diminished the weatherability of continental rocks and hence the strength of the silicate weathering feedback, allowing for the prolonged accumulation of volcanic CO2 in the oceans and atmosphere during the MECO. These results are supported by carbon cycle modeling simulations, which highlight the fundamental importance of a variable weathering feedback strength in climate and carbon cycle interactions in Earth's history.

Recruitment and Ongoing Engagement in a UK Smartphone Study Examining the Association Between Weather and Pain: Cohort Study.

BACKGROUND: The huge increase in smartphone use heralds an enormous opportunity for epidemiology research, but there is limited evidence regarding long-term engagement and attrition in mobile health (mHealth) studies. OBJECTIVE: The objective of this study was to examine how representative the Cloudy with a Chance of Pain study population is of wider chronic-pain populations and to explore patterns of engagement among participants during the first 6 months of the study. METHODS: Participants in the United Kingdom who had chronic pain (≥3 months) and enrolled between January 20, 2016 and January 29, 2016 were eligible if they were aged ≥17 years and used the study app to report any of 10 pain-related symptoms during the study period. Participant characteristics were compared with data from the Health Survey for England (HSE) 2011. Distinct clusters of engagement over time were determined using first-order hidden Markov models, and participant characteristics were compared between the clusters. RESULTS: Compared with the data from the HSE, our sample comprised a higher proportion of women (80.51%, 5129/6370 vs 55.61%, 4782/8599) and fewer persons at the extremes of age (16-34 and 75+). Four clusters of engagement were identified: high (13.60%, 865/6370), moderate (21.76%, 1384/6370), low (39.35%, 2503/6370), and tourists (25.44%, 1618/6370), between which median days of data entry ranged from 1 (interquartile range; IQR: 1-1; tourist) to 149 (124-163; high). Those in the high-engagement cluster were typically older, whereas those in the tourist cluster were mostly male. Few other differences distinguished the clusters. CONCLUSIONS: Cloudy with a Chance of Pain demonstrates a rapid and successful recruitment of a large, representative, and engaged sample of people with chronic pain and provides strong evidence to suggest that smartphones could provide a viable alternative to traditional data collection methods.

Mountain glaciation drives rapid oxidation of rock-bound organic carbon.

Over millions of years, the oxidation of organic carbon contained within sedimentary rocks is one of the main sources of carbon dioxide to the atmosphere, yet the controls on this emission remain poorly constrained. We use rhenium to track the oxidation of rock-bound organic carbon in the mountain watersheds of New Zealand, where high rates of physical erosion expose rocks to chemical weathering. Oxidative weathering fluxes are two to three times higher in watersheds dominated by valley glaciers and exposed to frost shattering processes, compared to those with less glacial cover; a feature that we also observe in mountain watersheds globally. Consequently, we show that mountain glaciation can result in an atmospheric carbon dioxide source during weathering and erosion, as fresh minerals are exposed for weathering in an environment with high oxygen availability. This provides a counter mechanism against global cooling over geological time scales.