Holocene evolution in weathering and erosion patterns in the Pearl River delta

Author Posting. © American Geophysical Union, 2013. This article is posted here by permission of American Geophysical Union for personal use, not for redistribution. The definitive version was published in Geochemistry, Geophysics, Geosystems 14 (2013); 2349–2368, doi:10.1002/ggge.20166.Sediments in the Pearl River delta have the potential to record the weathering response of this river basin to climate change since 9.5 ka, most notably weakening of the Asian monsoon since the Early Holocene (∼8 ka). Cores from the Pearl River delta show a clear temporal evolution of weathering intensity, as measured by K/Al, K/Rb, and clay mineralogy, that shows deposition of less weathered sediment at a time of weakening monsoon rainfall in the Early-Mid Holocene (6.0–2.5 ka). This may reflect an immediate response to a less humid climate, or more likely reduced reworking of older deposits from river terraces as the monsoon weakened. Human settlement of the Pearl River basin may have had a major impact on landscape and erosion as a result of the establishment of widespread agriculture. After around 2.5 ka weathering intensity sharply increased, despite limited change in the monsoon, but at a time when anthropogenic pollutants (e.g., Cu, Zn, and Pb) increased and when the flora of the basin changed. 87Sr/86Sr covaries with these other proxies but is also partly influenced by the presence of carbonate. The sediments in the modern Pearl River are even more weathered than the youngest material from the delta cores. We infer that the spread of farming into the Pearl River basin around 2.7 ka was followed by a widespread reworking of old, weathered soils after 2.5 ka, and large-scale disruption of the river system that was advanced by 2.0 ka.We acknowledge financial support from the Swire Educational Trust and South China Sea Institute of Oceanology PhD Funding (Grant No. MSGL09-06).2014-01-2

MLS Measurements of Stratospheric Hydrogen Cyanide During the 2015-2016 El Niño Event

It is known from ground-based measurements made during the 1982-1983 and 1997-1998 El Niño events that atmospheric hydrogen cyanide (HCN) tends to be higher during such years than at other times. The Microwave Limb Sounder (MLS) on the Aura satellite has been measuring HCN mixing ratios since launch in 2004; the measurements are ongoing at the time of writing. The winter of 2015- 2016 saw the largest El Niño event since 1997-1998. We present MLS measurements of HCN in the lower stratosphere for the Aura mission to date, comparing the 2015- 2016 El Niño period to the rest of the mission. HCN in 2015- 2016 is higher than at any other time during the mission, but ground-based measurements suggest that it may have been even more elevated in 1997-1998. As the MLS HCN data are essentially unvalidated, we show them alongside data from the MIPAS and ACE-FTS instruments; the three instruments agree reasonably well in the tropical lower stratosphere. Global HCN emissions calculated from the Global Fire Emissions Database (GFED v4.1) database are much greater during large El Niño events and are greater in 1997- 1998 than in 2015-2016, thereby showing good qualitative agreement with the measurements. Correlation between El Niño-Southern Oscillation (ENSO) indices, measured HCN, and GFED HCN emissions is less clear if the 2015-2016 event is excluded. In particular, the 2009-2010 winter had fairly strong El Niño conditions and fairly large GFED HCN emissions, but very little effect is observed in the MLS HCN

Comparison of foam swabs and toothbrushes as oral hygiene interventions in mechanically ventilated patients: a randomised split mouth study

Abstract Introduction During critical illness, dental plaque may serve as a reservoir of respiratory pathogens. This study compared the effectiveness of toothbrushing with a small-headed toothbrush or a foam-headed swab in mechanically ventilated patients. Methods This was a randomised, assessor-blinded, split-mouth trial, performed at a single critical care unit. Adult, orally intubated patients with >20 teeth, where >24 hours of mechanical ventilation was expected were included. Teeth were cleaned 12-hourly using a foam swab or toothbrush (each randomly assigned to one side of the mouth). Cleaning efficacy was based on plaque scores, gingival index and microbial plaque counts. Results High initial plaque (mean=2.1 (SD 0.45)) and gingival (mean=2.0 (SD 0.54)) scores were recorded for 21 patients. A significant reduction compared with initial plaque index occurred using both toothbrushes (mean change=−1.26, 95% CI −1.57 to −0.95; p<0.001) and foam swabs (mean change=−1.28, 95% CI −1.54 to −1.01; p<0.001). There was significant reduction in gingival index over time using toothbrushes (mean change=−0.92; 95% CI −1.19 to −0.64; p<0.001) and foam swabs (mean change=−0.85; 95% CI −1.10 to −0.61; p<0.001). Differences between cleaning methods were not statistically significant (p=0.12 for change in gingival index; p=0.24 for change in plaque index). There was no significant change in bacterial dental plaque counts between toothbrushing (mean change 3.7×104 colony-forming units (CFUs); minimum to maximum (−2.5×1010 CFUs, 8.7×107 CFUs)) and foam swabs (mean change 9×104 CFUs; minimum to maximum (−3.1×1010 CFUs, 3.0×107 CFUs)). Conclusions Patients admitted to adult intensive care had poor oral health, which improved after brushing with a toothbrush or foam swab. Both interventions were equally effective at removing plaque and reducing gingival inflammation. Trial registration number NCT01154257; Pre-results

Elective affinities of the Protestant ethic : Weber and the chemistry of capitalism

Peer reviewedPostprin

Intercomparison of long-term ground-based measurements of tropospheric and stratospheric ozone at Lauder, New Zealand (45S)

Long-term ground-based ozone measurements are crucial to study the recovery of stratospheric ozone as well as the trends of tropospheric ozone. This study is performed in the context of the LOTUS (Long-term Ozone Trends and Uncertainties in the Stratosphere) and TOAR-II (Tropospheric Ozone Assessment Report, phase II) initiatives. We perform an intercomparison study of total column ozone and multiple partial ozone columns between the ground-based measurements available at the Lauder station from 2000 to 2022, which are the Fourier transform infrared (FTIR) spectrometer, Dobson Umkehr, ozonesonde, lidar, and the microwave radiometer. We compare partial columns, defined to provide independent information: one tropospheric and three stratospheric columns. The intercomparison is analyzed using the median of relative differences (the bias) of FTIR with each of the other measurements, the scaled Median Absolute deviation (MADs), and a trend of these differences (measurement drift). The total column shows a bias and strong scatter well within the combined systematic and random uncertainties respectively. There is however a drift of 0.6&plusmn;0.5 %/decade if we consider the full time series. In the troposphere we find a low bias of -1.9 % with the ozonesondes. No drift is found between the three instruments in the troposphere, which is good for trend studies within TOAR-II. In both the lower and upper stratosphere, we get a negative bias for all instruments with respect to FTIR (between -1.2 % and -6.8 %), but all are within the range of the systematic uncertainties. In the middle stratosphere we seem to find a negative bias of around -5.2 to -6.6 %, pointing towards too high values for FTIR in this partial column. We find no significant drift in the stratosphere between ozonesonde and FTIR for all partial columns. We do observe drift between the FTIR and Umkehr partial columns in the lower and upper stratospheres (2.6&plusmn;1.1 %/decade and -3.2&plusmn;0.9 %/decade), with lidar in the midle and upper stratosphere (2.1&plusmn;0.8 %/decade and -3.7&plusmn;1.2 %/decade), and with MWR in the midle stratosphere (3.1&plusmn;1.7 %/decade). These drifts point to the fact that the different observed trends in LOTUS are not due to different sampling, vertical sensitivity or time periods and gaps. However, the difference in trends in LOTUS is reduced by applying a new FTIR retrieval strategy, which changes inputs such as the choice of microwindows, spectroscopy from HITRAN2008 to HITRAN2020, and the regularization method

Potential for diagnosis of infectious disease from the 100,000 Genomes Project Metagenomic Dataset: Recommendations for reporting results

The identification of microbiological infection is usually a diagnostic investigation, a complex process that is firstly initiated by clinical suspicion. With the emergence of high-throughput sequencing (HTS) technologies, metagenomic analysis has unveiled the power to identify microbial DNA/RNA from a diverse range of clinical samples (1). Metagenomic analysis of whole human genomes at the clinical/research interface bypasses the steps of clinical scrutiny and targeted testing and has the potential to generate unexpected findings relating to infectious and sometimes transmissible disease. There is no doubt that microbial findings that may have a significant impact on a patient’s treatment and their close contacts should be reported to those with clinical responsibility for the sample-donating patient. There are no clear recommendations on how such findings that are incidental, or outside the original investigation, should be handled. Here we aim to provide an informed protocol for the management of incidental microbial findings as part of the 100,000 Genomes Projectwhich may have broader application in this emerging field. As with any other clinical information, we aim to prioritise the reporting of data that are most likely to be of benefit to the patient and their close contacts. We also set out to minimize risks, costs and potential anxiety associated with the reporting of results that are unlikely to be of clinical significance. Our recommendations aim to support the practice of microbial metagenomics by providing a simplified pathway that can be applied to reporting the identification of potential pathogens from metagenomic datasets. Given that the ambition for UK sequenced human genomes over the next 5 years has been set to reach 5 million and the field of metagenomics is rapidly evolving, the guidance will be regularly reviewed and will likely adapt over time as experience develops

Correlations between comorbidities in trials and the community: an individual-level participant data meta-analysis

Background: People with comorbidities are under-represented in randomised controlled trials, and it is unknown whether patterns of comorbidity are similar in trials and the community. Methods: Individual-level participant data were obtained for 83 clinical trials (54,688 participants) for 16 index conditions from two trial repositories: Yale University Open Data Access (YODA) and the Centre for Global Clinical Research Data (Vivli). Community data (860,177 individuals) were extracted from the Secure Anonymised Information Linkage (SAIL) databank for the same index conditions. Comorbidities were defined using concomitant medications. For each index condition, we estimated correlations between comorbidities separately in trials and community data. For the six commonest comorbidities we estimated all pairwise correlations using Bayesian multivariate probit models, conditioning on age and sex. Correlation estimates from trials with the same index condition were combined into a single estimate. We then compared the trial and community estimates for each index condition. Results: Despite a higher prevalence of comorbidities in the community than in trials, the correlations between comorbidities were mostly similar in both settings. On comparing correlations between the community and trials, 21% of correlations were stronger in the community, 10% were stronger in the trials and 68% were similar in both. In the community, 5% of correlations were negative, 21% were null, 56% were weakly positive and 18% were strongly positive. Equivalent results for the trials were 11%, 33%, 45% and 10% respectively. Conclusions: Comorbidity correlations are generally similar in both the trials and community, providing some evidence for the reporting of comorbidity-specific findings from clinical trials

Representation of people with comorbidity and multimorbidity in clinical trials of novel drug therapies:an individual-level participant data analysis

Background: Clinicians are less likely to prescribe guideline-recommended treatments to people with multimorbidity than to people with a single condition. Doubts as to the applicability of clinical trials of drug treatments (the gold standard for evidence-based medicine) when people have co-existing diseases (comorbidity) may underlie this apparent reluctance. Therefore, for a range of index conditions, we measured the comorbidity among participants in clinical trials of novel drug therapies and compared this to the comorbidity among patients in the community. Methods: Data from industry-sponsored phase 3/4 multicentre trials of novel drug therapies for chronic medical conditions were identified from two repositories: Clinical Study Data Request and the Yale University Open Data Access project. We identified 116 trials (n = 122,969 participants) for 22 index conditions. Community patients were identified from a nationally representative sample of 2.3 million patients in Wales, UK. Twenty-one comorbidities were identified from medication use based on pre-specified definitions. We assessed the prevalence of each comorbidity and the total number of comorbidities (level of multimorbidity), for each trial and in community patients. Results: In the trials, the commonest comorbidities in order of declining prevalence were chronic pain, cardiovascular disease, arthritis, affective disorders, acid-related disorders, asthma/COPD and diabetes. These conditions were also common in community-based patients. Mean comorbidity count for trial participants was approximately half that seen in community-based patients. Nonetheless, a substantial proportion of trial participants had a high degree of multimorbidity. For example, in asthma and psoriasis trials, 10–15% of participants had ≥ 3 conditions overall, while in osteoporosis and chronic obstructive pulmonary disease trials 40–60% of participants had ≥ 3 conditions overall. Conclusions: Comorbidity and multimorbidity are less common in trials than in community populations with the same index condition. Comorbidity and multimorbidity are, nevertheless, common in trials. This suggests that standard, industry-funded clinical trials are an underused resource for investigating treatment effects in people with comorbidity and multimorbidity

Assessing trial representativeness using Serious Adverse Events : An observational analysis using aggregate and individual-level data from clinical trials and routine healthcare data

Background: The applicability of randomised controlled trials of pharmacological agents to older people with frailty/multimorbidity is often uncertain, due to concerns that trials are not representative. However, assessing trial representativeness is challenging and complex. We explore an approach assessing trial representativeness by comparing rates of trial serious adverse events (SAE) to rates of hospitalisation/death in routine care. Methods: This was an observational analysis of individual (125 trials, n=122,069) and aggregate-level drug trial data (483 trials, n=636,267) for 21 index conditions compared to population-based routine healthcare data (routine care). Trials were identified from ClinicalTrials.gov. Routine care comparison from linked primary care and hospital data from Wales, UK (n=2.3M). Our outcome of interest was SAEs (routinely reported in trials). In routine care, SAEs were based on hospitalisations and deaths (which are SAEs by definition). We compared trial SAEs in trials to expected SAEs based on age/sex standardised routine care populations with the same index condition. Using IPD, we assessed the relationship between multimorbidity count and SAEs in both trials and routine care and assessed the impact on the observed/expected SAE ratio additionally accounting for multimorbidity. Results: For 12/21 index conditions, the pooled observed/expected SAE ratio was &lt;1, indicating fewer SAEs in trial participants than in routine care. A further 6/21 had point estimates &lt;1 but the 95% CI included the null. The median pooled estimate of observed/expected SAE ratio was 0.60 (95% CI 0.55–0.64; COPD) and the interquartile range was 0.44 (0.34–0.55; Parkinson’s disease) to 0.87 (0.58–1.29; inflammatory bowel disease). Higher multimorbidity count was associated with SAEs across all index conditions in both routine care and trials. For most trials, the observed/expected SAE ratio moved closer to 1 after additionally accounting for multimorbidity count, but it nonetheless remained below 1 for most. Conclusions: Trial participants experience fewer SAEs than expected based on age/sex/condition hospitalisation and death rates in routine care, confirming the predicted lack of representativeness. This difference is only partially explained by differences in multimorbidity. Assessing observed/expected SAE may help assess the applicability of trial findings to older populations in whom multimorbidity and frailty are common