Yes
Bell Beaker pottery spread across western and central Europe beginning around 2750 BCE before disappearing between 2200-1800 BCE. The mechanism of its expansion is a topic of long-standing ...debate, with support for both cultural diffusion and human migration. We present new genome-wide ancient DNA data from 170 Neolithic, Copper Age and Bronze Age Europeans, including 100 Beaker-associated individuals. In contrast to the Corded Ware Complex, which has previously been identified as arriving in central Europe following migration from the east, we observe limited genetic affinity between Iberian and central European Beaker Complex-associated individuals, and thus exclude migration as a significant mechanism of spread between these two regions. However, human migration did have an important role in the further dissemination of the Beaker Complex, which we document most clearly in Britain using data from 80 newly reported individuals dating to 3900-1200 BCE. British Neolithic farmers were genetically similar to contemporary populations in continental Europe and in particular to Neolithic Iberians, suggesting that a portion of the farmer ancestry in Britain came from the Mediterranean rather than the Danubian route of farming expansion. Beginning with the Beaker period, and continuing through the Bronze Age, all British individuals harboured high proportions of Steppe ancestry and were genetically closely related to Beaker-associated individuals from the Lower Rhine area. We use these observations to show that the spread of the Beaker Complex to Britain was mediated by migration from the continent that replaced >90% of Britain's Neolithic gene pool within a few hundred years, continuing the process that brought Steppe ancestry into central and northern Europe 400 years earlier.
Climate factors have been shown to be associated with spontaneous musculoskeletal and some surgical site infections with increased rates of infection during warmer periods. To date, little research ...has been performed to determine if this phenomenon is associated with differences in the risk of revision for prosthetic joint infection (PJI) in primary TKA.
(1) Does the rate of revision for early PJI within the first year after primary TKA differ between tropical and nontropical regions? (2) Is there a seasonal variation in the rate of revision for PJI? (3) Is the geographic and seasonal variation (if present) associated with the sex, age, and/or American Society of Anesthesiologists (ASA) grade of the patient?
All 219,983 primary TKAs performed for osteoarthritis over a 5-year period (2011-2015) in the Australian Orthopaedic Association National Joint Replacement Registry were examined based on the month of the primary procedure to determine the rate of revision for PJI within 12 months. The data were analyzed to determine the differences in the risk of revision for PJI based on geographic region and season of the primary procedure adjusting for sex, age, and ASA grade of the patient.
The early revision rate for PJI was higher in the tropical compared with the nontropical region of Australia (0.73% versus 0.37%; odds ratio OR, 1.87; 95% confidence interval CI, 1.44-2.42; p < 0.001). The tropical region of Australia demonstrated a seasonal variation in the rate of revision for PJI with a higher rate during the warmer monsoon wet season of summer and fall (summer/fall 0.98% versus winter/spring 0.51%; OR, 1.88; 95% CI, 1.12-3.16; p = 0.02). A seasonal variation was not seen in the nontropical region (OR, 1.03; 95% CI, 0.90-1.19; p = 0.64). The regional and seasonal changes were independent of sex, age, and ASA grade.
Climate factors are associated with the risk of early revision for PJI in patients undergoing primary TKA with rates of such revisions approximately double in tropical regions compared with nontropical regions. Additionally, tropical regions demonstrate a seasonal variation with the risk of PJI doubling during the warmer, monsoonal wet season of summer and fall. These findings should be confirmed in further studies that can better control for possible confounding variables. The mechanism for this phenomenon is not clear, and further research into this subject is also indicated.
Level III, therapeutic study.
Determining mean transit times in headwater catchments is critical for understanding catchment functioning and understanding their responses to changes in landuse or climate. Determining whether mean ...transit times (MTTs) correlate with drainage density, slope angle, area, or land cover permits a better understanding of the controls on water flow through catchments and allows first‐order predictions of MTTs in other catchments to be made. This study assesses whether there are identifiable controls on MTTs determined using 3H in headwater catchments of southeast Australia. Despite MTTs at baseflow varying from a few years to >100 years, it was difficult to predict MTTs using single or groups of readily‐measured catchment attributes. The lack of readily‐identifiable correlations hampers the prediction of MTTs in adjacent catchments even where these have similar geology, land use, and topography. The long MTTs of the Australian headwater catchments are probably in part due to the catchments having high storage volumes in deeply‐weathered regolith, combined with low recharge rates due to high evapotranspiration. However, the difficulty in estimating storage volumes at the catchment scale hampers the use of this parameter to estimate MTTs. The runoff coefficient (the fraction of rainfall exported via the stream) is probably also controlled by evapotranspiration and recharge rates. Correlations between the runoff coefficient and MTTs in individual catchments allow predictions of MTTs in nearby catchments to be made. MTTs are shorter in high rainfall periods as the catchments wet up and shallow water stores are mobilized. Despite the contribution of younger water, the major ion geochemistry in individual catchments commonly does not correlate with MTTs, probably reflecting heterogeneous reactions and varying degrees of evapotranspiration. Documenting MTTs in catchments with high storage volumes and/or low recharge rates elsewhere is important for understanding MTTs in diverse environments.
Mean transit times estimated using tritium in Australian headwater catchments at baseflow conditions are years to decades. The long mean transit times result from a deeply‐weathered regolith coupled with high evapotranspiration rates. The large catchment storages buffer the streamflow against the impact of short‐term climate variability.
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BFBNIB, FZAB, GIS, IJS, KILJ, NLZOH, NUK, OILJ, SBCE, SBMB, UL, UM, UPUK
The Seasonal Variation of TKR Infection Rates Parkinson, Ben; Armit, Drew; Reid, Michael ...
Orthopaedic journal of sports medicine,
05/2017, Volume:
5, Issue:
5_suppl5
Journal Article
Peer reviewed
Open access
Introduction:
A seasonal variation in the incidence of surgical site infections has been described following a number of common surgical procedures. However in the setting of elective Total Knee ...Replacement (TKR), the role of environmental factors is an area of research that is currently lacking from the literature. Data recently presented from our institution demonstrates a possible trend toward higher infection rates during periods of increased temperature and humidity. The aim of this study was to investigate if seasonal and geographical factors influence the rate of TKR infection within Australia.
Methods:
Data from the AOA National Joint Registry for all primary TKRs performed within the last 5 years was analysed to determine the revision rates for early (<12 months) post-operative infection. The infection rates for tropical regions (Darwin, Cairns, Townsville, Mackay) were compared to the remainder of the country. A month-by-month analysis was performed to determine if there was a seasonal variation within the 2 study groups.
Results:
During the study period a total of 207,540 primary TKRs were performed (6,514 tropical vs 201,026 non-tropical regions). Overall, the rate of revision for infection was significantly higher for the tropical regions of Australia (0.80% vs 0.39%). In non-tropical regions, there was no observed seasonal variation of infection rates. In tropical regions, there was a clear seasonal variation found, with the winter months being associated with a significantly lower rate of infection than the remainder of the year (0.37% vs 0.94%). The infection rates were not significantly different between tropical (0.37%) and non- tropical (0.38%) regions during the winter period.
Conclusion:
To the best of our knowledge, this is the first study to investigate and demonstrate a significant influence from seasonal variation on primary TKR infection rates. This phenomenon is evident only within tropical regions, with the periods of warmer and humid weather resulting in a significantly increased risk of primary TKR infection. These findings require further investigation and research.
With a large increase in the volume and type of data archived in GigaScience Database (GigaDB) since its launch in 2011, we have studied the metrics and user patterns to assess the important aspects ...needed to best suit current and future use. This has led to new front-end developments and enhanced interactivity and functionality that greatly improve user experience. In this article, we present an overview of the current practices including the Biocurational role of the GigaDB staff, the broad usage metrics of GigaDB datasets and an update on how the GigaDB platform has been overhauled and enhanced to improve the stability and functionality of the codebase. Finally, we report on future directions for the GigaDB resource.
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IZUM, KILJ, NUK, PILJ, PNG, SAZU, UL, UM, UPUK
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