Micron-scale plasma membrane curvature is recognized by the septin cytoskeleton

© The Author(s), 2016. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Journal of Cell Biology 213 (2016): 23-32, doi: 10.1083/jcb.201512029.Cells change shape in response to diverse environmental and developmental conditions, creating topologies with micron-scale features. Although individual proteins can sense nanometer-scale membrane curvature, it is unclear if a cell could also use nanometer-scale components to sense micron-scale contours, such as the cytokinetic furrow and base of neuronal branches. Septins are filament-forming proteins that serve as signaling platforms and are frequently associated with areas of the plasma membrane where there is micron-scale curvature, including the cytokinetic furrow and the base of cell protrusions. We report here that fungal and human septins are able to distinguish between different degrees of micron-scale curvature in cells. By preparing supported lipid bilayers on beads of different curvature, we reconstitute and measure the intrinsic septin curvature preference. We conclude that micron-scale curvature recognition is a fundamental property of the septin cytoskeleton that provides the cell with a mechanism to know its local shape.This work was supported by grants from the National Science Foundation (MCB-507511 to A.S. Gladfelter) and the National Institutes of Health (NIGMS-T32GM008704 to A.A. Bridges)

The Vehicle, Spring 1985

Vol. 26, No. 2 Table of Contents Beyond the FieldsKeila Tooleypage 3 Lonely Sculptor Accustomed to Living AloneMichelle Mitchellpage 4 Mona LisaBob Zordanipage 4 Poet Born in Pearl HarborAngelique Jenningspage 5 IntroductionsGraham Lewispage 6 Living InsideJennifer Soulepage 9 PictureKathy Greypage 10 Salvadore Dali in a Wheelchair on TVAngelique Jenningspage 11 Sonata in E FlatBecky Lawsonpage 12 Myopia and Wild KingdomMichelle Mitchellpage 12 On Becoming a GrandmotherKeila Tooleypage 13 A VisionJennifer D. Pringlepage 14 The Covered BridgeDebbie Woodleypage 14 Jacob\u27s LifeJoan Sebastianpage 15 ForgotGraham Lewispage 15 A Dozen and One TrainsongsAngelique Jenningspage 16 Women\u27s PlaceJennifer Soulepage 19 Night SailingKim Dumentatpage 20 She Isn\u27t There WhenMichelle Mitchellpage 20 A Case for the Common ColdMaggie Kennedypage 21 the cityTammy Batespage 22 The RattlesnakeEric S. McGeepage 22 New PictureKeila Tooleypage 23 Lewis and SinGraham Lewispage 24 Funny BarbecueBob Zordanipage 26 In a DreamF. Link Rapierpage 26 The Winter\u27s ColdJennifer Soulepage 27 Diary EntryTammy Batespage 27 Minor God and Patron Saint of Rabbits SpeaksAngelique Jenningspage 28 A MomentBrett Wilhelmpage 29 The Bishop SeatF. Link Rapierpage 30 The Thought of Being Rid of MyselfKeila Tooleypage 33 I Saw A ChildBea Cessnapage 33 Complacent gourmetGary Burrowspage 34 Night DreamsJennifer Soulepage 35 Changing ImagesAmy Callpage 35 Olsen Rug Co. Waterfall & ParkMaggie Kennedypage 36 Edge of the WildF. Link Rapierpage 37 DragonS. Hillpage 37 Harvests of CornBob Zordanipage 38 The Club JeromeGary Burrowspage 39 Tarzan And The CabPatrick Peterspage 39 The Rain That Never CameLynanne Feilenpage 40 Wonderment of the Far CrescentF. Link Rapierpage 40https://thekeep.eiu.edu/vehicle/1047/thumbnail.jp

Electrocardiographic Deep Learning for Predicting Post-Procedural Mortality

Background. Pre-operative risk assessments used in clinical practice are limited in their ability to identify risk for post-operative mortality. We hypothesize that electrocardiograms contain hidden risk markers that can help prognosticate post-operative mortality. Methods. In a derivation cohort of 45,969 pre-operative patients (age 59+- 19 years, 55 percent women), a deep learning algorithm was developed to leverage waveform signals from pre-operative ECGs to discriminate post-operative mortality. Model performance was assessed in a holdout internal test dataset and in two external hospital cohorts and compared with the Revised Cardiac Risk Index (RCRI) score. Results. In the derivation cohort, there were 1,452 deaths. The algorithm discriminates mortality with an AUC of 0.83 (95% CI 0.79-0.87) surpassing the discrimination of the RCRI score with an AUC of 0.67 (CI 0.61-0.72) in the held out test cohort. Patients determined to be high risk by the deep learning model's risk prediction had an unadjusted odds ratio (OR) of 8.83 (5.57-13.20) for post-operative mortality as compared to an unadjusted OR of 2.08 (CI 0.77-3.50) for post-operative mortality for RCRI greater than 2. The deep learning algorithm performed similarly for patients undergoing cardiac surgery with an AUC of 0.85 (CI 0.77-0.92), non-cardiac surgery with an AUC of 0.83 (0.79-0.88), and catherization or endoscopy suite procedures with an AUC of 0.76 (0.72-0.81). The algorithm similarly discriminated risk for mortality in two separate external validation cohorts from independent healthcare systems with AUCs of 0.79 (0.75-0.83) and 0.75 (0.74-0.76) respectively. Conclusion. The findings demonstrate how a novel deep learning algorithm, applied to pre-operative ECGs, can improve discrimination of post-operative mortality

Does Private Islamic Schooling Promote Terrorism? An Analysis of the Educational Background of Successful American Homegrown Terrorists

Some commentators argue that private religious schools are less likely to inculcate the attributes of good citizenship than traditional public schools, specifically proposing that private Islamic schools are relatively more likely to produce individuals sympathetic to terrorism. This study offers a preliminary examination of the question by studying the educational backgrounds of Western educated terrorists. While data are limited, in accord with prior work findings indicate the vast majority of both Islamic and reactionary terrorists attended traditional public schools and had no religious education; hence findings suggest that early religious training and identification may actually encourage prosocial behavior

Effect of Hydrocortisone on Mortality and Organ Support in Patients With Severe COVID-19: The REMAP-CAP COVID-19 Corticosteroid Domain Randomized Clinical Trial.

Importance: Evidence regarding corticosteroid use for severe coronavirus disease 2019 (COVID-19) is limited. Objective: To determine whether hydrocortisone improves outcome for patients with severe COVID-19. Design, Setting, and Participants: An ongoing adaptive platform trial testing multiple interventions within multiple therapeutic domains, for example, antiviral agents, corticosteroids, or immunoglobulin. Between March 9 and June 17, 2020, 614 adult patients with suspected or confirmed COVID-19 were enrolled and randomized within at least 1 domain following admission to an intensive care unit (ICU) for respiratory or cardiovascular organ support at 121 sites in 8 countries. Of these, 403 were randomized to open-label interventions within the corticosteroid domain. The domain was halted after results from another trial were released. Follow-up ended August 12, 2020. Interventions: The corticosteroid domain randomized participants to a fixed 7-day course of intravenous hydrocortisone (50 mg or 100 mg every 6 hours) (n = 143), a shock-dependent course (50 mg every 6 hours when shock was clinically evident) (n = 152), or no hydrocortisone (n = 108). Main Outcomes and Measures: The primary end point was organ support-free days (days alive and free of ICU-based respiratory or cardiovascular support) within 21 days, where patients who died were assigned -1 day. The primary analysis was a bayesian cumulative logistic model that included all patients enrolled with severe COVID-19, adjusting for age, sex, site, region, time, assignment to interventions within other domains, and domain and intervention eligibility. Superiority was defined as the posterior probability of an odds ratio greater than 1 (threshold for trial conclusion of superiority >99%). Results: After excluding 19 participants who withdrew consent, there were 384 patients (mean age, 60 years; 29% female) randomized to the fixed-dose (n = 137), shock-dependent (n = 146), and no (n = 101) hydrocortisone groups; 379 (99%) completed the study and were included in the analysis. The mean age for the 3 groups ranged between 59.5 and 60.4 years; most patients were male (range, 70.6%-71.5%); mean body mass index ranged between 29.7 and 30.9; and patients receiving mechanical ventilation ranged between 50.0% and 63.5%. For the fixed-dose, shock-dependent, and no hydrocortisone groups, respectively, the median organ support-free days were 0 (IQR, -1 to 15), 0 (IQR, -1 to 13), and 0 (-1 to 11) days (composed of 30%, 26%, and 33% mortality rates and 11.5, 9.5, and 6 median organ support-free days among survivors). The median adjusted odds ratio and bayesian probability of superiority were 1.43 (95% credible interval, 0.91-2.27) and 93% for fixed-dose hydrocortisone, respectively, and were 1.22 (95% credible interval, 0.76-1.94) and 80% for shock-dependent hydrocortisone compared with no hydrocortisone. Serious adverse events were reported in 4 (3%), 5 (3%), and 1 (1%) patients in the fixed-dose, shock-dependent, and no hydrocortisone groups, respectively. Conclusions and Relevance: Among patients with severe COVID-19, treatment with a 7-day fixed-dose course of hydrocortisone or shock-dependent dosing of hydrocortisone, compared with no hydrocortisone, resulted in 93% and 80% probabilities of superiority with regard to the odds of improvement in organ support-free days within 21 days. However, the trial was stopped early and no treatment strategy met prespecified criteria for statistical superiority, precluding definitive conclusions. Trial Registration: ClinicalTrials.gov Identifier: NCT02735707

Determining crystal structures through crowdsourcing and coursework

We show here that computer game players can build high-quality crystal structures. Introduction of a new feature into the computer game Foldit allows players to build and real-space refine structures into electron density maps. To assess the usefulness of this feature, we held a crystallographic model-building competition between trained crystallographers, undergraduate students, Foldit players and automatic model-building algorithms. After removal of disordered residues, a team of Foldit players achieved the most accurate structure. Analysing the target protein of the competition, YPL067C, uncovered a new family of histidine triad proteins apparently involved in the prevention of amyloid toxicity. From this study, we conclude that crystallographers can utilize crowdsourcing to interpret electron density information and to produce structure solutions of the highest quality

The contribution of X-linked coding variation to severe developmental disorders

Abstract: Over 130 X-linked genes have been robustly associated with developmental disorders, and X-linked causes have been hypothesised to underlie the higher developmental disorder rates in males. Here, we evaluate the burden of X-linked coding variation in 11,044 developmental disorder patients, and find a similar rate of X-linked causes in males and females (6.0% and 6.9%, respectively), indicating that such variants do not account for the 1.4-fold male bias. We develop an improved strategy to detect X-linked developmental disorders and identify 23 significant genes, all of which were previously known, consistent with our inference that the vast majority of the X-linked burden is in known developmental disorder-associated genes. Importantly, we estimate that, in male probands, only 13% of inherited rare missense variants in known developmental disorder-associated genes are likely to be pathogenic. Our results demonstrate that statistical analysis of large datasets can refine our understanding of modes of inheritance for individual X-linked disorders

Physics in anaesthesia

This chapter covers the basic science of physics relevant to anaesthetic practice. Equipment and measurement devices are covered elsewhere. Starting with fundamentals, atomic structure is introduced, followed by dimensions and units as used in science. Basic mechanics are then discussed, focusing on mass and density, force, pressure, energy, and power. The concept of linearity, hysteresis, and frequency response in physical systems is then introduced, using relevant examples, which are easy to understand. Laminar and turbulent fluid flow is then described, using flow measurement devices as applications of this theory. The concept of pressure and its measurement is then discussed in some detail, including partial pressure. Starting with the kinetic theory of gases, heat and temperature are described, along with the gas laws, critical temperature, sublimation, latent heat, vapour pressure and vaporization illustrated by the function of anaesthetic vaporizers, humidity, solubility, diffusion, osmosis, and osmotic pressure. Ultrasound and its medical applications are discussed in some detail, including Doppler and its use to measure flow. This is followed by an introduction to lasers and their medical uses. The final subject covered is electricity, starting with concepts of charge and current, voltage, energy, and power, and the role of magnetism. This is followed by a discussion of electrical circuits and the rules governing them, and bridge circuits used in measurement. The function of capacitors and inductors is then introduced, and alternating current and transformers are described.</p

Intraoperative monitoring

Chapter 25 introduced some basic generic principles applicable to many measurement and monitoring techniques. Chapter 43 introduces those principles not covered in Chapter 25 and discusses in detail the clinical applications and limitations of the many monitoring techniques available to the modern clinical anaesthetist. It starts with non-invasive blood pressure measurement, including clinical and automated techniques. This is followed by techniques of direct blood pressure measurement, noting that transducers and calibration have been discussed in Chapter 25. This is followed by electrocardiography. There then follows a section on the different methods of measuring cardiac output, including the pulmonary artery catheter, the application of ultrasound in echocardiography, pulse contour analysis (LiDCO™ and PiCCO™), and transthoracic electrical impedance. Pulse oximetry is then discussed in some detail. Depth of anaesthesia monitoring is then described, starting with the electroencephalogram and its application in BIS™ monitors, the use of evoked potentials, and entropy. There then follow sections on gas pressure measurement in cylinders and in breathing systems, followed by gas volume and flow measurement, including the rotameter, spirometry, and the pneumotachograph, and the measurement of lung dead space and functional residual capacity using body plethysmography and dilution techniques. The final section is on respiratory gas analysis, starting with light refractometry as the standard against which other techniques are compared, infrared spectroscopy, mass spectrometry, and Raman spectroscopy (the principles of these techniques having been introduced in Chapter 25), piezoelectric and paramagnetic analysers, polarography and fuel cells, and blood gas analysis.</p