Abscess Formation Following Spilled Gallstones During Laparoscopic Cholecystectomy

A review of the English literature concerning spilled gallstones during laparoscopic cholecystectomy is compared with one institution's experience of four cases during 1,726 laparoscopic cholecystectomies performed over a four-year period. Strategies regarding management and treatment are discussed

Three-section terahertz quantum cascade lasers with externally biased photonic lattices

Single-mode (SM) operation of terahertz (THz)-frequency quantum cascade lasers (QCLs) is essential for gas sensing and for their use as local oscillators. Whilst such SM operation has been demonstrated previously using distributed feedback gratings and photonic lattices (PLs), an ability to tune SM THz QCLs electronically would be highly desirable for many applications. This work investigates the electronic properties of multiple segment QCLs, and explores possible electronic tuning mechanisms. We have fabricated THz QCLs with PL gratings. Our THz QCLs, based on a bound-to-continuum active region design, were processed into 150-μm-wide surface-plasmon ridge waveguides with lengths 2.5–4.5 mm. The upper waveguide was then patterned into three sections. The outer sections were fully metallised with gold. In the central 530-μm-long section, however, the top n-doped layer was etched away and metallic PLs fabricated using electron-beam lithography, with grating pitch between 14.60 and 15.65 μm. In each device, both the PL and the outer sections were bonded separately to allow independent biasing. Devices were cooled in a continuous-flow helium cryostat and emission spectra measured using a Fourier-transform infrared spectrometer. Standard (unpatterned) devices lased with multiple Fabry–Pérot modes in the range 2.70–2.83 THz, whilst single mode emission was observed for devices patterned with a PL (Fig. 2). The emission spectra recorded from devices with PLs of different grating pitch showed stop-band behaviour, with the experimentally-determined stop-band bandwidth of around 25 GHz agreeing with the value of 23 GHz calculated using finite-element modelling. Light-output–current–voltage (LIV) characteristics were acquired under different configurations, including the central PL sections both biased and unbiased. An equivalent circuit model describing the electronic properties of the three-section devices was developed, and was found to reproduce the experimental observations well

Infinite-period density-matrix model for terahertz-frequency quantum cascade lasers

In this work we present a density matrix model which considers an infinite quantum cascade laser (QCL) and models transport via a nearest neighbor approximation. We will discuss derivation of output parameters of the model in detail and show the direct mathematical link to semi-classical rate equation approach. This model can be extended to an arbitrary number of states in the QCL period, without a priori specification of upper and lower lasing level. Application of the model to various QCL structures is possible, including bound-to-continuum structures which typically employ a large number of states per period. The model has been applied to a 2 THz bound-to-continuum QCL and a very good agreement with measured V-I characteristics is obtained along with qualitative agreement with measured L-I characteristics in terms of dynamic range

The 2017 terahertz science and technology roadmap

Science and technologies based on terahertz frequency electromagnetic radiation (100 GHz-30 THz) have developed rapidly over the last 30 years. For most of the 20th Century, terahertz radiation, then referred to as sub-millimeter wave or far-infrared radiation, was mainly utilized by astronomers and some spectroscopists. Following the development of laser based terahertz time-domain spectroscopy in the 1980s and 1990s the field of THz science and technology expanded rapidly, to the extent that it now touches many areas from fundamental science to 'real world' applications. For example THz radiation is being used to optimize materials for new solar cells, and may also be a key technology for the next generation of airport security scanners. While the field was emerging it was possible to keep track of all new developments, however now the field has grown so much that it is increasingly difficult to follow the diverse range of new discoveries and applications that are appearing. At this point in time, when the field of THz science and technology is moving from an emerging to a more established and interdisciplinary field, it is apt to present a roadmap to help identify the breadth and future directions of the field. The aim of this roadmap is to present a snapshot of the present state of THz science and technology in 2017, and provide an opinion on the challenges and opportunities that the future holds. To be able to achieve this aim, we have invited a group of international experts to write 18 sections that cover most of the key areas of THz science and technology. We hope that The 2017 Roadmap on THz science and technology will prove to be a useful resource by providing a wide ranging introduction to the capabilities of THz radiation for those outside or just entering the field as well as providing perspective and breadth for those who are well established. We also feel that this review should serve as a useful guide for government and funding agencies

Botrytis species on bulb crops

Abstract. A number of Botrytis species are pathogens of bulb crops. Botrytis squamosa (teleomorph=Botrytotinia squamosa) causal agent of botrytis leaf blight and B. allii the causal agent of botrytis neck rotare two of the most important fungal diseases of onion. The taxonomics of several of the neck rotpathogens of onion have been revised on the basis of recent molecular sequence analysis studies. B. allii,B. aclada, and B. byssoidea are now recognized as distinct species causing neck rot diseases of onion. B.cinerea is also pathogenic on onion, primarily causing botrytis brown stain on onion bulbs. B. tulipae, B.elliptica, and B. gladiolorum are important pathogens of flower bulbs and are the causal agents of leafblight in tulip, lily, and gladiolus, respectively. Leaf blight in the major flower bulb crops is called ‘fire’referring to the fire-like symptoms occurring on the leaves of flower bulb plants when epidemics occur inproduction fields. In both the onion and flower bulb production systems chemicals are still heavily reliedupon to control the major diseases, however, alternative disease management systems also are used andundoubtedly will become increasingly important in controlling the diseases. Infected plants and colonizedplant debris are considered important sources of inoculum for B. squamosa, B. tulipae, and B. elliptica,particularly when sclerotia are formed. Sclerotia of B. squamosa serve as the source of conidia, as well asapothecia producing ascospores, in onion production areas in New York. The primary inoculum sourcesof B. allii and B. gladiolorum are believed to be infested seed and infected corms, respectively

GWAS and meta-analysis identifies 49 genetic variants underlying critical COVID-19

Data availability: Downloadable summary data are available through the GenOMICC data site (https://genomicc.org/data). Summary statistics are available, but without the 23andMe summary statistics, except for the 10,000 most significant hits, for which full summary statistics are available. The full GWAS summary statistics for the 23andMe discovery dataset will be made available through 23andMe to qualified researchers under an agreement with 23andMe that protects the privacy of the 23andMe participants. For further information and to apply for access to the data, see the 23andMe website (https://research.23andMe.com/dataset-access/). All individual-level genotype and whole-genome sequencing data (for both academic and commercial uses) can be accessed through the UKRI/HDR UK Outbreak Data Analysis Platform (https://odap.ac.uk). A restricted dataset for a subset of GenOMICC participants is also available through the Genomics England data service. Monocyte RNA-seq data are available under the title ‘Monocyte gene expression data’ within the Oxford University Research Archives (https://doi.org/10.5287/ora-ko7q2nq66). Sequencing data will be made freely available to organizations and researchers to conduct research in accordance with the UK Policy Framework for Health and Social Care Research through a data access agreement. Sequencing data have been deposited at the European Genome–Phenome Archive (EGA), which is hosted by the EBI and the CRG, under accession number EGAS00001007111.Extended data figures and tables are available online at https://www.nature.com/articles/s41586-023-06034-3#Sec21 .Supplementary information is available online at https://www.nature.com/articles/s41586-023-06034-3#Sec22 .Code availability: Code to calculate the imputation of P values on the basis of SNPs in linkage disequilibrium is available at GitHub (https://github.com/baillielab/GenOMICC_GWAS).Acknowledgements: We thank the members of the Banco Nacional de ADN and the GRA@CE cohort group; and the research participants and employees of 23andMe for making this work possible. A full list of contributors who have provided data that were collated in the HGI project, including previous iterations, is available online (https://www.covid19hg.org/acknowledgements).Change history: 11 July 2023: A Correction to this paper has been published at: https://doi.org/10.1038/s41586-023-06383-z. -- In the version of this article initially published, the name of Ana Margarita Baldión-Elorza, of the SCOURGE Consortium, appeared incorrectly (as Ana María Baldion) and has now been amended in the HTML and PDF versions of the article.Copyright © The Author(s) 2023, Critical illness in COVID-19 is an extreme and clinically homogeneous disease phenotype that we have previously shown1 to be highly efficient for discovery of genetic associations2. Despite the advanced stage of illness at presentation, we have shown that host genetics in patients who are critically ill with COVID-19 can identify immunomodulatory therapies with strong beneficial effects in this group3. Here we analyse 24,202 cases of COVID-19 with critical illness comprising a combination of microarray genotype and whole-genome sequencing data from cases of critical illness in the international GenOMICC (11,440 cases) study, combined with other studies recruiting hospitalized patients with a strong focus on severe and critical disease: ISARIC4C (676 cases) and the SCOURGE consortium (5,934 cases). To put these results in the context of existing work, we conduct a meta-analysis of the new GenOMICC genome-wide association study (GWAS) results with previously published data. We find 49 genome-wide significant associations, of which 16 have not been reported previously. To investigate the therapeutic implications of these findings, we infer the structural consequences of protein-coding variants, and combine our GWAS results with gene expression data using a monocyte transcriptome-wide association study (TWAS) model, as well as gene and protein expression using Mendelian randomization. We identify potentially druggable targets in multiple systems, including inflammatory signalling (JAK1), monocyte–macrophage activation and endothelial permeability (PDE4A), immunometabolism (SLC2A5 and AK5), and host factors required for viral entry and replication (TMPRSS2 and RAB2A).GenOMICC was funded by Sepsis Research (the Fiona Elizabeth Agnew Trust), the Intensive Care Society, a Wellcome Trust Senior Research Fellowship (to J.K.B., 223164/Z/21/Z), the Department of Health and Social Care (DHSC), Illumina, LifeArc, the Medical Research Council, UKRI, a BBSRC Institute Program Support Grant to the Roslin Institute (BBS/E/D/20002172, BBS/E/D/10002070 and BBS/E/D/30002275) and UKRI grants MC_PC_20004, MC_PC_19025, MC_PC_1905 and MRNO2995X/1. A.D.B. acknowledges funding from the Wellcome PhD training fellowship for clinicians (204979/Z/16/Z), the Edinburgh Clinical Academic Track (ECAT) programme. This research is supported in part by the Data and Connectivity National Core Study, led by Health Data Research UK in partnership with the Office for National Statistics and funded by UK Research and Innovation (grant MC_PC_20029). Laboratory work was funded by a Wellcome Intermediate Clinical Fellowship to B.F. (201488/Z/16/Z). We acknowledge the staff at NHS Digital, Public Health England and the Intensive Care National Audit and Research Centre who provided clinical data on the participants; and the National Institute for Healthcare Research Clinical Research Network (NIHR CRN) and the Chief Scientist’s Office (Scotland), who facilitate recruitment into research studies in NHS hospitals, and to the global ISARIC and InFACT consortia. GenOMICC genotype controls were obtained using UK Biobank Resource under project 788 funded by Roslin Institute Strategic Programme Grants from the BBSRC (BBS/E/D/10002070 and BBS/E/D/30002275) and Health Data Research UK (HDR-9004 and HDR-9003). UK Biobank data were used in the GSMR analyses presented here under project 66982. The UK Biobank was established by the Wellcome Trust medical charity, Medical Research Council, Department of Health, Scottish Government and the Northwest Regional Development Agency. It has also had funding from the Welsh Assembly Government, British Heart Foundation and Diabetes UK. The work of L.K. was supported by an RCUK Innovation Fellowship from the National Productivity Investment Fund (MR/R026408/1). J.Y. is supported by the Westlake Education Foundation. SCOURGE is funded by the Instituto de Salud Carlos III (COV20_00622 to A.C., PI20/00876 to C.F.), European Union (ERDF) ‘A way of making Europe’, Fundación Amancio Ortega, Banco de Santander (to A.C.), Cabildo Insular de Tenerife (CGIEU0000219140 ‘Apuestas científicas del ITER para colaborar en la lucha contra la COVID-19’ to C.F.) and Fundación Canaria Instituto de Investigación Sanitaria de Canarias (PIFIISC20/57 to C.F.). We also acknowledge the contribution of the Centro National de Genotipado (CEGEN) and Centro de Supercomputación de Galicia (CESGA) for funding this project by providing supercomputing infrastructures. A.D.L. is a recipient of fellowships from the National Council for Scientific and Technological Development (CNPq)-Brazil (309173/2019-1 and 201527/2020-0)