Explore the vast range of titles available.

PurposeThe McMaster Monitoring My Mobility (MacM3) study aims to understand trajectories of mobility decline in later life using multisensor wearable technology. To our knowledge, MacM3 is the first major cohort to combine accelerometry and a Global Positioning System (GPS) to track real-world mobility in community-dwelling older adults.ParticipantsBetween May 2022 and May 2024, MacM3 recruited 1555 community-dwelling older adults (mean age 73.9 years, SD=5.5) from Hamilton and Toronto, Ontario. Of the cohort, 68.4% were female, 62.4% married/partnered, 75.3% had post-secondary education and 62.9% had≥3 comorbidities. Most were Canadian born (69.4%) and white/Caucasian (88.0%), with greater ethnocultural diversity observed at the Toronto site.Findings to dateAt baseline, 56.7% of participants reported no mobility limitations, 15.9% had preclinical limitations and 27.4% had minor mobility limitations. Mean gait speed for the total sample was 1.23 m/s, with a mean Timed Up and Go time of 9.4 s and a 5× sit-to-stand time of 13.0 s. A total of 1301 participants had valid wrist-worn device data, and 1008 participants who agreed to wear the thigh-worn device had valid data (≥7 days with ≥10 hours of wear per day). Step count data (n=1008) revealed a mean of 8437 steps per day (SD=2943), with 5073 steps in the lowest quartile and 12 303 steps in the highest.Future plansOngoing work aims to develop predictive models of mobility decline by integrating wearable, clinical and environmental data. Pipeline enhancements will enable GPS/inertial measurement unit fusion to explore mobility-environment interactions and support ageing-in-place tools.

Red and processed meat is considered risk factors of gestational diabetes mellitus (GDM), but the evidence is inconclusive. We aimed to examine the association between red and processed meat intake and odds of GDM among South Asian and White European women living in Canada. This is a cross-sectional analysis of pregnant women from two birth cohorts: SouTh Asian biRth cohorT (START; n = 976) and Family Atherosclerosis Monitoring In earLY life (FAMILY; n = 581). Dietary intake was assessed using a validated 169-item semi-quantitative food-frequency questionnaire (FFQ). Multivariate logistic regression models were used to examine the associations between gestational diabetes and: 1) total red and processed meat; 2) unprocessed red meat; 3) processed meat and GDM after adjustment for potential confounders. There were 241 GDM cases in START and 91 in FAMILY. The median total red and processed meat intake were 1.5 g/d (START) and 52.8 g/d (FAMILY). In START, the multivariable-adjusted odds ratio (OR) showed neither lower nor higher intakes of unprocessed red meat (p-trend = 0.68), processed meat (p-trend = 0.90), or total red and processed meat (p-trend = 0.44), were associated with increased odds of GDM, when compared with medium intake. Similar results were observed in FAMILY except for processed meat intake [OR = 0.94 (95% CI 0.47-1.91), for medium versus low and OR = 1.51 (95% CI 0.77-2.29) for medium versus high; p-trend = 0.18] after adjusting for additional dietary factors such as the diet quality score, total fiber, saturated fat and glycemic load. Medium compared with low or high red and processed meat intake is not associated with GDM in White Europeans and South Asians living in Canada.

Accurate and timely detection, quantification, and attribution of methane emissions from Underground Gas Storage (UGS) facilities is essential for improving confidence in greenhouse gas inventories, enabling emission mitigation by facility operators, and supporting efforts to assess facility integrity and safety. We conducted multiple airborne surveys of the 12 active UGS facilities in California between January 2016 and November 2017 using advanced remote sensing and in situ observations of near-surface atmospheric methane (CH4). These measurements where combined with wind data to derive spatially and temporally resolved methane emission estimates for California UGS facilities and key components with spatial resolutions as small as 1-3 m and revisit intervals ranging from minutes to months. The study spanned normal operations, malfunctions, and maintenance activity from multiple facilities including the active phase of the Aliso Canyon blowout incident in 2016 and subsequent return to injection operations in summer 2017. We estimate that the net annual methane emissions from the UGS sector in California averaged between 11.0 3.8 GgCH4 yr−1 (remote sensing) and 12.3 3.8 GgCH4 yr−1 (in situ). Net annual methane emissions for the 7 facilities that reported emissions in 2016 were estimated between 9.0 3.2 GgCH4 yr−1 (remote sensing) and 9.5 3.2 GgCH4 yr−1 (in situ), in both cases around 5 times higher than reported. The majority of methane emissions from UGS facilities in this study are likely dominated by anomalous activity: higher than expected compressor loss and leaking bypass isolation valves. Significant variability was observed at different time-scales: daily compressor duty-cycles and infrequent but large emissions from compressor station blow-downs. This observed variability made comparison of remote sensing and in situ observations challenging given measurements were derived largely at different times, however, improved agreement occurred when comparing simultaneous measurements. Temporal variability in emissions remains one of the most challenging aspects of UGS emissions quantification, underscoring the need for more systematic and persistent methane monitoring.

Methane (CH4), an important greenhouse gas and pollutant, has been targeted for mitigation. Our recent California airborne survey identified >500 CH4 point source super-emitters, which accounted for 34%-46% of the statewide CH4 emissions inventory for 2016 (Duren et al 2019 Nature 575 180-184). Individual plumes were observed in close proximity to expected methane emitting infrastructure, including gas storage facilities, hydrocarbon storage tanks, landfills, dairy lagoons, and pipeline leaks. In order to systematically attribute these plumes to their sources, we developed Vista-CA a geospatial database, that contains more than 900 000 validated CH4 infrastructure elements in the state of California. In parallel, we developed a complimentary algorithm that attributes any individual CH4 plume observation to the most likely Vista-CA source with 99% accuracy. The present study illustrates the capabilities of the Vista-CA CH4 database along with the Airborne Visible/Infrared Imaging Spectrometer-Next Generation airborne CH4 retrievals to locate and attribute CH4 point sources to specific economic sectors to improve the state CH4 budget and identify mitigation targets.

Childhood obesity is a world-wide concern due to its growing prevalence and association with cardiometabolic risk factors in childhood and subsequent adult cardiovascular disease. In young pre-school children, there is uncertainty regarding which of the commonly used anthropometric measures of childhood obesity is best associated with cardiometabolic risk factors. This study compared the utility of common measures used in identifying obesity in these young children. The four commonly used metrics for identifying obesity in children: body fat percentage ≥ 90th percentile, waist circumference ≥ 90th percentile, BMI z score > 2 SD and waist-to-height ratio (WHtR) ≥ 0.5, were measured in a cohort of children born singleton, at full term and followed from birth (n = 761) to 5 years of age (n = 513). The utility of each in identifying cardiometabolic risk factors (fasting lipid profile, fasting blood glucose and blood pressure) was examined. At age 5 years, children with percent body fat ≥ 90th percentile or waist circumference ≥ 90th percentile, were associated with higher levels of triglycerides, glucose, and systolic and diastolic blood pressures than those < 90th percentile, respectively. Such differences were not obvious at age 3 years or at birth. A BMI z-score > 2 SD was associated with higher levels of triglycerides and systolic and diastolic blood pressure but not glucose at age 5 years. Differences in HDL cholesterol, fasting glucose and systolic blood pressure were observed in children with BMI z score > 2 SD at age 3 years but not with the other indices of obesity. As almost all children had WHtR ≥ 0.5 at birth, ages 1 and 3 years, this measure could not differentiate increased cardiometabolic risk. At age 5 years, the differences were much more obvious, with significant differences in triglycerides and systolic and diastolic blood pressures between those with WHtR ≥ 0.5 and those with < 0.5. Each of the four commonly used measures of childhood obesity shows moderate associations with cardiometabolic risk factors at 5 years, with no advantage of one measure over the other. These associations were less consistent at 3 years of age or younger. These observations have not been reported previously.

Introduction Obesity, a complex and multifactorial disease, is defined by a body mass index (BMI) greater than 30 kg/m². When the BMI exceeds 40 kg/m², it is classified as morbid obesity. This condition leads to excessive fat accumulation, which impairs normal body function and metabolism. For individuals grappling with morbid obesity and those who have faced significant hurdles in their quest for substantial weight loss, bariatric surgery emerges as a vital option. Purpose The study aims to explore the dynamics of bariatric surgical tourism in Pakistan, shedding light on factors influencing the choice of Pakistan as a destination for bariatric tourism. Materials and methods A retrospective cross-sectional study design was adopted. Data were gathered from the medical records database, including all patients who had undergone bariatric surgery from 2018 until 2022. The data collection process involved comprehensive patient outreach, where investigators conducted phone interviews and collected patient satisfaction assessments. During these phone interviews, valuable information was gathered by posing questions. These inquiries encompassed various aspects, including the patient's overall satisfaction with the surgical experience, their countries of origin, the specific bariatric procedures they underwent, the motivating factors behind their decision to travel abroad for surgery, their postoperative follow-up routines, and any complications they may have encountered. Results One hundred and nine patients traveled to Pakistan for bariatric surgery from 2018 to 2022. Out of 109 patients, 78 responded to the questionnaire by phone or email. The proforma was filled by 41 (52.5%) males and 37 (47.5%) females. Forty-seven (60.2%) of these patients underwent Roux-en-Y gastric bypass and 31 (39.8%) patients underwent sleeve gastrectomy. Out of 78 bariatric patients, 72 (92.3%) were satisfied with their surgery, five patients (6.4%) were neutral in their response and one patient (1.3%) was dissatisfied with the surgery. Most of the patients (26, 33.3%) declared money as the main driving force for traveling, with long waiting times being the close second reason (19, 24.36%) patients. Conclusion At least 2% of worldwide bariatric procedures are provided for medical tourists. Countries such as Mexico, India, Lebanon, and Romania dominate as providers for patients mainly from the USA, UK, and Germany. The lack of affordable bariatric healthcare and long waiting lists are some of the reasons for patients choosing bariatric tourism. The 92.3% satisfaction rate of patients with the surgery and its outcomes is a significant finding, as it suggests that bariatric surgery services provided in Pakistan are meeting or exceeding the expectations of international patients. The exceptionally high level of patient satisfaction speaks to the quality of care provided by the medical institutions in Pakistan. The data and analysis presented in this study shed light on the motivations and experiences of international patients traveling to Pakistan for bariatric surgery. These insights are invaluable for healthcare providers, policymakers, and the medical tourism industry as they seek to enhance the accessibility, affordability, and quality of healthcare services for domestic and international patients.

Methane is a powerful greenhouse gas and is targeted for emissions mitigation by the US state of California and other jurisdictions worldwide 1 , 2 . Unique opportunities for mitigation are presented by point-source emitters—surface features or infrastructure components that are typically less than 10 metres in diameter and emit plumes of highly concentrated methane 3 . However, data on point-source emissions are sparse and typically lack sufficient spatial and temporal resolution to guide their mitigation and to accurately assess their magnitude 4 . Here we survey more than 272,000 infrastructure elements in California using an airborne imaging spectrometer that can rapidly map methane plumes 5 – 7 . We conduct five campaigns over several months from 2016 to 2018, spanning the oil and gas, manure-management and waste-management sectors, resulting in the detection, geolocation and quantification of emissions from 564 strong methane point sources. Our remote sensing approach enables the rapid and repeated assessment of large areas at high spatial resolution for a poorly characterized population of methane emitters that often appear intermittently and stochastically. We estimate net methane point-source emissions in California to be 0.618 teragrams per year (95 per cent confidence interval 0.523–0.725), equivalent to 34–46 per cent of the state’s methane inventory 8 for 2016. Methane ‘super-emitter’ activity occurs in every sector surveyed, with 10 per cent of point sources contributing roughly 60 per cent of point-source emissions—consistent with a study of the US Four Corners region that had a different sectoral mix 9 . The largest methane emitters in California are a subset of landfills, which exhibit persistent anomalous activity. Methane point-source emissions in California are dominated by landfills (41 per cent), followed by dairies (26 per cent) and the oil and gas sector (26 per cent). Our data have enabled the identification of the 0.2 per cent of California’s infrastructure that is responsible for these emissions. Sharing these data with collaborating infrastructure operators has led to the mitigation of anomalous methane-emission activity 10 . Emission of methane from ‘point sources’—small surface features or infrastructure components—is monitored with an airborne spectrometer, identifying possible targets for mitigation efforts.

Methane (CH4), an important greenhouse gas and pollutant, has been targeted for mitigation. Our recent California airborne survey identified >500 CH4 point sources, which accounted for 34-46% of the statewide CH4 emissions inventory for 2016. Individual plumes were observed in close proximity to expected CH4 emitting infrastructure. In order to systematically attribute these plumes to their sources, we developed Vista-CA a geospatial database, that contains more than 900,000 validated CH4 infrastructure elements in the state of California. In parallel, we developed a complimentary algorithm that attributes any individual CH4 plume observation to high confidence Vista-CA source with 99% accuracy. This research illustrates the capabilities of the Vista-CA CH4 database along with Airborne Visible/Infrared Imaging Spectrometer – Next Generation’s (AVIRIS-NG) airborne CH4 retrievals to locate and attribute CH4 point sources to specific economic sectors. Additionally, this research delivers two emissions products for Kern County: a top-down estimate called the AVIRIS-NG Source Data product and the Vista-CA Bottom-Up emissions dataset. We found general agreement in the source apportionment and magnitudes between these datasets. Moreover, due to current CH4 inventories having large uncertainties in emissions from the energy processing and production sectors where fugitive emissions predominate, we used airborne CH4 imaging survey data to show that CH4 emissions from power plants in California are underestimated by current CH4 inventory approaches. We developed process-based bottom-up emission estimates for over 300 power plants in California using Intergovernmental Panel on Climate Change (IPCC) methods. We used airborne CH4 imaging to attribute CH4 observations to over 250 California power plants and characterize the frequency and persistency of top-down CH4 emissions. We found that fugitive emissions constitute 90% of total observed emissions from power plants with the remainder derived from process-driven activity while bottom-up emissions are 28 – 54 times smaller than top-down observations. Comparing the inventory-based estimates with observations, the data show “super-emitter” behavior with 60% of total power plant emissions coming from a handful of facilities, likely due to fugitive CH4 emissions. Future inventories should take advantage of emission observations to quantify CH4 from these sources to improve the state CH4 budget and identify mitigation targets.

The extent to which variation in food-related metabolites are attributable to non-dietary factors remains unclear, which may explain inconsistent food-metabolite associations observed in population studies. This study examined the association between non-dietary factors and the serum concentrations of food-related biomarkers and quantified the amount of variability in metabolite concentrations explained by non-dietary factors. Pregnant women (n = 600) from two Canadian birth cohorts completed a validated semi-quantitative food frequency questionnaire, and serum metabolites were measured by multisegment injection-capillary electrophoresis-mass spectrometry. Hierarchical linear modelling and principal component partial R-square (PC-PR2) were used for data analysis. For proline betaine and DHA (mainly exogenous), citrus foods and fish/fish oil intake, respectively, explained the highest proportion of variability relative to non-dietary factors. The unique contribution of dietary factors was similar (15:0, 17:0, hippuric acid, TMAO) or lower (14:0, tryptophan betaine, 3-methylhistidine, carnitine) compared to non-dietary factors (i.e., ethnicity, maternal age, gestational age, pre-pregnancy BMI, physical activity, and smoking) for metabolites that can either be produced endogenously, biotransformed by gut microbiota, and/or derived from multiple food sources. The results emphasize the importance of adjusting for non-dietary factors in future analyses to improve the accuracy and precision of the measures of food intake and their associations with health and disease.

Dairies emit roughly half of total methane (CH4) emissions in California, generating CH4 from both enteric fermentation by ruminant gut microbes and anaerobic decomposition of manure. Representation of these emission processes is essential for management and mitigation of CH4 emissions and is typically done using standardized emission factors applied at large spatial scales (e.g., state level). However, CH4-emitting activities and management decisions vary across facilities, and current inventories do not have sufficiently high spatial resolution to capture changes at this scale. Here, we develop a spatially explicit database of dairies in California, with information from operating permits and California-specific reports detailing herd demographics and manure management at the facility scale. We calculated manure management and enteric fermentation CH4 emissions using two previously published bottom-up approaches and a new farm-specific calculation developed in this work. We also estimate the effect of mitigation strategies – the use of mechanical separators and installation of anaerobic digesters – on CH4 emissions. We predict that implementation of digesters at the 106 dairies that are existing or planned in California will reduce manure CH4 emissions from those facilities by an average of 26 % and total state CH4 emissions by 5 % (or ∼36.5 Gg CH4/yr). In addition to serving as a planning tool for mitigation, this database is useful as a prior for atmospheric observation-based emissions estimates, attribution of emissions to a specific facility, and validation of CH4 emissions reductions from management changes. Raster files of the datasets and associated metadata are available from the Oak Ridge National Laboratory Distributed Active Archive Center for Biogeochemical Dynamics (ORNL DAAC; Marklein and Hopkins, 2020; https://doi.org/10.3334/ORNLDAAC/1814).