Neolithic and Bronze Age migration to Ireland and establishment of the insular Atlantic genome

The Neolithic and Bronze Age transitions were profound cultural shifts catalyzed in parts of Europe by migrations, first of early farmers from the Near East and then Bronze Age herders from the Pontic Steppe. However, a decades-long, unresolved controversy is whether population change or cultural adoption occurred at the Atlantic edge, within the British Isles. We address this issue by using the first whole genome data from prehistoric Irish individuals. A Neolithic woman (3343–3020 cal BC) from a megalithic burial (10.3× coverage) possessed a genome of predominantly Near Eastern origin. She had some hunter–gatherer ancestry but belonged to a population of large effective size, suggesting a substantial influx of early farmers to the island. Three Bronze Age individuals from Rathlin Island (2026–1534 cal BC), including one high coverage (10.5×) genome, showed substantial Steppe genetic heritage indicating that the European population upheavals of the third millennium manifested all of the way from southern Siberia to the western ocean. This turnover invites the possibility of accompanying introduction of Indo-European, perhaps early Celtic, language. Irish Bronze Age haplotypic similarity is strongest within modern Irish, Scottish, and Welsh populations, and several important genetic variants that today show maximal or very high frequencies in Ireland appear at this horizon. These include those coding for lactase persistence, blue eye color, Y chromosome R1b haplotypes, and the hemochromatosis C282Y allele; to our knowledge, the first detection of a known Mendelian disease variant in prehistory. These findings together suggest the establishment of central attributes of the Irish genome 4,000 y ago

Man of Letters, Literary Lady, Journalist or Reporter?

The enormous changes wrought in the British newspaper industry during the late nineteenth and early twentieth centuries brought about a revolution in newspaper reading habits, financing and influence, all aspects of which have been well-documented by historians of the press. But what of the contributor, particularly the freelance whose millions of words formed, mostly anonymously, the content of the new mass market press? How did writers negotiate changes in the literary marketplace during this time as editors demanded more ‘news’ and less in the way of whimsical paragraphing, and sketches, the traditional newspaper output of the professional man, or woman, of letters? Through the study of memoirs, correspondence and the fictional output of contributors to the press during this time, it is possible to discern the often fraught relations between writers and their most lucrative market

Performance of a Discrete Wavelet Transform for Compressing Plasma Count Data and its Application to the Fast Plasma Investigation on NASA's Magnetospheric Multiscale Mission

Plasma measurements in space are becoming increasingly faster, higher resolution, and distributed over multiple instruments. As raw data generation rates can exceed available data transfer bandwidth, data compression is becoming a critical design component. Data compression has been a staple of imaging instruments for years, but only recently have plasma measurement designers become interested in high performance data compression. Missions will often use a simple lossless compression technique yielding compression ratios of approximately 2:1, however future missions may require compression ratios upwards of 10:1. This study aims to explore how a Discrete Wavelet Transform combined with a Bit Plane Encoder (DWT/BPE), implemented via a CCSDS standard, can be used effectively to compress count information common to plasma measurements to high compression ratios while maintaining little or no compression error. The compression ASIC used for the Fast Plasma Investigation (FPI) on board the Magnetospheric Multiscale mission (MMS) is used for this study. Plasma count data from multiple sources is examined: resampled data from previous missions, randomly generated data from distribution functions, and simulations of expected regimes. These are run through the compression routines with various parameters to yield the greatest possible compression ratio while maintaining little or no error, the latter indicates that fully lossless compression is obtained. Finally, recommendations are made for future missions as to what can be achieved when compressing plasma count data and how best to do so

Effects of antiplatelet therapy on stroke risk by brain imaging features of intracerebral haemorrhage and cerebral small vessel diseases: subgroup analyses of the RESTART randomised, open-label trial

Background Findings from the RESTART trial suggest that starting antiplatelet therapy might reduce the risk of recurrent symptomatic intracerebral haemorrhage compared with avoiding antiplatelet therapy. Brain imaging features of intracerebral haemorrhage and cerebral small vessel diseases (such as cerebral microbleeds) are associated with greater risks of recurrent intracerebral haemorrhage. We did subgroup analyses of the RESTART trial to explore whether these brain imaging features modify the effects of antiplatelet therapy

Regulatory Polymorphisms in Human DBH Affect Peripheral Gene Expression and Sympathetic Activity

Dopamine β-hydroxylase (DBH) catalyzes the conversion of dopamine to norepinephrine in the CNS and peripherally. DBH variants are associated with large changes in circulating DBH and implicated in multiple disorders; yet causal relationships and tissue-specific effects remain unresolved

AhR transcriptional activity in serum of Inuits across Greenlandic districts

<p>Abstract</p> <p>Background</p> <p>Human exposure to lipophilic persistent organic pollutants (POPs) including polychlorinated dibenzo-<it>p</it>-dioxins/furans (PCDDs/PCDFs), polychlorinated biphenyls (PCBs) and organochlorine pesticide is ubiquitous. The individual is exposed to a complex mixture of POPs being life-long beginning during critical developmental windows. Exposure to POPs elicits a number of species- and tissue-specific toxic responses, many of which involve the aryl hydrocarbon receptor (AhR). The aim of this study was to compare the actual level of integrated AhR transcriptional activity in the lipophilic serum fraction containing the actual POP mixture among Inuits from different districts in Greenland, and to evaluate whether the AhR transactivity is correlated to the bio-accumulated POPs and/or lifestyle factors.</p> <p>Methods</p> <p>The study included 357 serum samples from the Greenlandic districts: Nuuk and Sisimiut (South West Coast), Qaanaaq (North Coast) and Tasiilaq (East Coast). The bio-accumulated serum POPs were extracted by ethanol: hexane and clean-up on Florisil columns. Effects of the serum extract on the AhR transactivity was determined using the Hepa 1.12cR mouse hepatoma cell line carrying an AhR-luciferase reporter gene, and the data was evaluated for possible association to the serum levels of 14 PCB congeners, 10 organochlorine pesticide residues and/or lifestyle factors.</p> <p>Results</p> <p>In total 85% of the Inuit samples elicited agonistic AhR transactivity in a district dependent pattern. The median level of the AhR-TCDD equivalent (AhR-TEQ) of the separate genders was similar in the different districts. For the combined data the order of the median AhR-TEQ was Tasiilaq > Nuuk ≥ Sisimiut > Qaanaaq possibly being related to the different composition of POPs. In overall, the AhR transactivity was inversely correlated to the levels of sum POPs, age and/or intake of marine food.</p> <p>Conclusion</p> <p>i) We observed that the proportion of dioxin like (DL) compounds in the POP mixture was the dominating factor affecting the level of serum AhR transcriptional activity even at very high level of non DL-PCBs; ii) The inverse association between the integrated serum AhR transactivity and sum of POPs might be explained by the higher level of compounds antagonizing the AhR function probably due to selective POP bioaccumulation in the food chain.</p

The development and validation of a scoring tool to predict the operative duration of elective laparoscopic cholecystectomy

Background: The ability to accurately predict operative duration has the potential to optimise theatre efficiency and utilisation, thus reducing costs and increasing staff and patient satisfaction. With laparoscopic cholecystectomy being one of the most commonly performed procedures worldwide, a tool to predict operative duration could be extremely beneficial to healthcare organisations. Methods: Data collected from the CholeS study on patients undergoing cholecystectomy in UK and Irish hospitals between 04/2014 and 05/2014 were used to study operative duration. A multivariable binary logistic regression model was produced in order to identify significant independent predictors of long (> 90 min) operations. The resulting model was converted to a risk score, which was subsequently validated on second cohort of patients using ROC curves. Results: After exclusions, data were available for 7227 patients in the derivation (CholeS) cohort. The median operative duration was 60 min (interquartile range 45–85), with 17.7% of operations lasting longer than 90 min. Ten factors were found to be significant independent predictors of operative durations > 90 min, including ASA, age, previous surgical admissions, BMI, gallbladder wall thickness and CBD diameter. A risk score was then produced from these factors, and applied to a cohort of 2405 patients from a tertiary centre for external validation. This returned an area under the ROC curve of 0.708 (SE = 0.013, p  90 min increasing more than eightfold from 5.1 to 41.8% in the extremes of the score. Conclusion: The scoring tool produced in this study was found to be significantly predictive of long operative durations on validation in an external cohort. As such, the tool may have the potential to enable organisations to better organise theatre lists and deliver greater efficiencies in care

Evaluation of individual and ensemble probabilistic forecasts of COVID-19 mortality in the United States

Short-term probabilistic forecasts of the trajectory of the COVID-19 pandemic in the United States have served as a visible and important communication channel between the scientific modeling community and both the general public and decision-makers. Forecasting models provide specific, quantitative, and evaluable predictions that inform short-term decisions such as healthcare staffing needs, school closures, and allocation of medical supplies. Starting in April 2020, the US COVID-19 Forecast Hub (https://covid19forecasthub.org/) collected, disseminated, and synthesized tens of millions of specific predictions from more than 90 different academic, industry, and independent research groups. A multimodel ensemble forecast that combined predictions from dozens of groups every week provided the most consistently accurate probabilistic forecasts of incident deaths due to COVID-19 at the state and national level from April 2020 through October 2021. The performance of 27 individual models that submitted complete forecasts of COVID-19 deaths consistently throughout this year showed high variability in forecast skill across time, geospatial units, and forecast horizons. Two-thirds of the models evaluated showed better accuracy than a naïve baseline model. Forecast accuracy degraded as models made predictions further into the future, with probabilistic error at a 20-wk horizon three to five times larger than when predicting at a 1-wk horizon. This project underscores the role that collaboration and active coordination between governmental public-health agencies, academic modeling teams, and industry partners can play in developing modern modeling capabilities to support local, state, and federal response to outbreaks

The United States COVID-19 Forecast Hub dataset

Academic researchers, government agencies, industry groups, and individuals have produced forecasts at an unprecedented scale during the COVID-19 pandemic. To leverage these forecasts, the United States Centers for Disease Control and Prevention (CDC) partnered with an academic research lab at the University of Massachusetts Amherst to create the US COVID-19 Forecast Hub. Launched in April 2020, the Forecast Hub is a dataset with point and probabilistic forecasts of incident cases, incident hospitalizations, incident deaths, and cumulative deaths due to COVID-19 at county, state, and national, levels in the United States. Included forecasts represent a variety of modeling approaches, data sources, and assumptions regarding the spread of COVID-19. The goal of this dataset is to establish a standardized and comparable set of short-term forecasts from modeling teams. These data can be used to develop ensemble models, communicate forecasts to the public, create visualizations, compare models, and inform policies regarding COVID-19 mitigation. These open-source data are available via download from GitHub, through an online API, and through R packages

Recommendations for effective documentation in regional anesthesia: an expert panel Delphi consensus project

Background and objectives: Documentation is important for quality improvement, education, and research. There is currently a lack of recommendations regarding key aspects of documentation in regional anesthesia. The aim of this study was to establish recommendations for documentation in regional anesthesia. Methods: Following the formation of the executive committee and a directed literature review, a long list of potential documentation components was created. A modified Delphi process was then employed to achieve consensus amongst a group of international experts in regional anesthesia. This consisted of 2 rounds of anonymous electronic voting and a final virtual round table discussion with live polling on items not yet excluded or accepted from previous rounds. Progression or exclusion of potential components through the rounds was based on the achievement of strong consensus. Strong consensus was defined as ≥75% agreement and weak consensus as 50%-74% agreement. Results: Seventy-seven collaborators participated in both rounds 1 and 2, while 50 collaborators took part in round 3. In total, experts voted on 83 items and achieved a strong consensus on 51 items, weak consensus on 3 and rejected 29. Conclusion: By means of a modified Delphi process, we have established expert consensus on documentation in regional anesthesia