300 research outputs found

    Modern meat: the next generation of meat from cells

    Get PDF
    Modern Meat is the first textbook on cultivated meat, with contributions from over 100 experts within the cultivated meat community. The Sections of Modern Meat comprise 5 broad categories of cultivated meat: Context, Impact, Science, Society, and World. The 19 chapters of Modern Meat, spread across these 5 sections, provide detailed entries on cultivated meat. They extensively tour a range of topics including the impact of cultivated meat on humans and animals, the bioprocess of cultivated meat production, how cultivated meat may become a food option in Space and on Mars, and how cultivated meat may impact the economy, culture, and tradition of Asia

    Predicting and preventing relapse of depression in primary care: a mixed methods study

    Get PDF
    BackgroundMost people with depression are managed in primary care. Relapse (reemergence of depression symptoms after improvement) is common and contributes to the burden and morbidity associated with depression. There is a lack of evidence-based approaches for risk-stratifying people according to risk of relapse and for preventing relapse in primary care.MethodsIn this mixed methods study, I initially reviewed studies looking to predict relapse of depression across all settings. I then attempted to derive and validate a prognostic model to predict relapse within 6-8 months in a primary care setting, using multilevel logistic regression analysis on individual participant data from seven studies (n=1244). Concurrently, a qualitative workstream, using thematic analysis, explored the perspectives of general practitioners (GPs) and people with lived experience of depression around relapse risk and prevention in practice.ResultsThe systematic review identified eleven models; none could currently be implemented in a primary care setting. The prognostic model developed in this study had inadequate predictive performance on internal validation (Cstatistic 0.60; calibration slope 0.81). I carried out twenty-two semi-structured interviews with GPs and twenty-three with people with lived experience of depression. People with lived experience of depression and GPs reflected that a discussion around relapse would be useful but was not routinely offered. Both participant groups felt there would be benefits to relapse prevention for depression being embedded within primary care.ConclusionsWe are currently unable to accurately predict an individual’s risk ofdepression relapse. The longer-term care of people with depression ingeneral practice could be improved by enabling continuity of care, increased consistency and clarity around follow-up arrangements, and focussed discussions around relapse risk and prevention. Scalable, brief relapse prevention interventions are needed, which would require policy change and additional resource. We need to better understand existing interventions and barriers to implementation in practice

    Does Anyone Else? The Lived Experience of Writing About Depersonalization on Reddit

    Get PDF
    As both The International Statistical Classification of Diseases and Related Health Problems 11th Revision (ICD-11) and The Diagnostic and Statistical Manual of Mental Disorders-5-Text Revision (DSM-5-TR) describe, depersonalization (DP) involves unpleasant episodes of detachment from one’s sense of self or of unreality in the environment. Symptoms include people feeling as though they are an outside observer of their thoughts, feelings, sensations, body, or actions. DP can take the form of emotional numbing, in which people may feel they are watching themselves from a distance or as though they are characters in a play. People can also feel physically numb, disconnected from parts of their own bodies to the degree that they feel as though they are observing the world from behind glass, as if through the lens of a camera, or within a dream (World Health Organization, 2019; American Psychiatric Association, 2022). DP is a form of dissociation and a common protective response to trauma. People experiencing DP symptoms report a wide spectrum of distortions and impairments to affective, cognitive, and physiological/perceptual functioning. A common experience is fear that what they are experiencing is a sign of irreversible brain damage, and a belief that DP symptoms indicate progression toward insanity is also common. When DP symptoms are misinterpreted either as indicative of severe mental illness or brain dysfunction, a vicious cycle of increasing anxiety and consequently increasing DP symptoms can result. This might lead to avoiding situations known to cause symptoms to escalate. On the clinician side, many without experience with DP might think patients’ descriptions are metaphorical, or they might misinterpret them as psychotic symptoms. Resulting misdiagnoses can lead to ineffectual treatment and prolonged distress. Trends suggest people are increasingly seeking mental health-related information online. Some research suggests others seek first-person perspectives. A prime place for sharing such experiences is Reddit, a social media platform. Through thematic analysis of posts, themes might emerge that might serve to inform mental health professionals about people’s lived experiences with DP symptoms and suggest new questions to ask about symptoms not yet well understood by many. This dissertation is available in open access at AURA (https://aura.antioch.edu) and OhioLINK ETD Center (https://etd.ohiolink.edu)

    Surgical skills modeling in cardiac ablation using deep learning

    Get PDF
    Cardiovascular diseases, a leading global cause of death, can be treated using Minimally Invasive Surgery (MIS) for various heart conditions. Cardiac ablation is an example of MIS, treating heart rhythm disorders like atrial fibrillation and the operation outcomes are highly dependent on the surgeon's skills. This procedure utilizes catheters, flexible endovascular devices inserted into the patient's blood vessels through a small incision. Traditionally, novice surgeons' performance is assessed in the Operating Room (OR) through surgical tasks. Unskilled behavior can lead to longer operations and inferior surgical outcomes. However, an alternative approach can be capturing surgeons' maneuvers and using them as input for an AI model to evaluate their skills outside the OR. To this end, two experimental setups were proposed to study the skills modelling for surgical behaviours. The first setup simulates the ablation procedure using a mechanical system with a synthetic heartbeat mechanism that measures contact forces between the catheter's tip and tissue. The second one simulates the cardiac catheterization procedure for the surgeon’s practice and records the user's maneuvers at the same time. The first task involved maintaining the force within a safe range while the tip of the catheter is touching the surface. The second task was passing a catheter’s tip through curves and level-intersection on a transparent blood vessel phantom. To evaluate attendees' demonstrations, it is crucial to extract maneuver models for both expert and novice surgeons. Data from participants, including novices and experts, performing the task using the experimental setups, is compiled. Deep recurrent neural networks are employed to extract the model of skills by solving a binary classification problem, distinguishing between expert and novice maneuvers. The results demonstrate the proposed networks' ability to accurately distinguish between novice and expert surgical skills, achieving an accuracy of over 92%

    Ultrasound Guidance in Perioperative Care

    Get PDF

    Brain Computations and Connectivity [2nd edition]

    Get PDF
    This is an open access title available under the terms of a CC BY-NC-ND 4.0 International licence. It is free to read on the Oxford Academic platform and offered as a free PDF download from OUP and selected open access locations. Brain Computations and Connectivity is about how the brain works. In order to understand this, it is essential to know what is computed by different brain systems; and how the computations are performed. The aim of this book is to elucidate what is computed in different brain systems; and to describe current biologically plausible computational approaches and models of how each of these brain systems computes. Understanding the brain in this way has enormous potential for understanding ourselves better in health and in disease. Potential applications of this understanding are to the treatment of the brain in disease; and to artificial intelligence which will benefit from knowledge of how the brain performs many of its extraordinarily impressive functions. This book is pioneering in taking this approach to brain function: to consider what is computed by many of our brain systems; and how it is computed, and updates by much new evidence including the connectivity of the human brain the earlier book: Rolls (2021) Brain Computations: What and How, Oxford University Press. Brain Computations and Connectivity will be of interest to all scientists interested in brain function and how the brain works, whether they are from neuroscience, or from medical sciences including neurology and psychiatry, or from the area of computational science including machine learning and artificial intelligence, or from areas such as theoretical physics

    Predicting and preventing relapse of depression in primary care: a mixed methods study

    Get PDF
    Background Most people with depression are managed in primary care. Relapse (re-emergence of depression symptoms after improvement) is common and contributes to the burden and morbidity associated with depression. There is a lack of evidence-based approaches for risk-stratifying people according to risk of relapse and for preventing relapse in primary care. Methods In this mixed methods study, I initially reviewed studies looking to predict relapse of depression across all settings. I then attempted to derive and validate a prognostic model to predict relapse within 6-8 months in a primary care setting, using multilevel logistic regression analysis on individual participant data from seven studies (n=1244). Concurrently, a qualitative workstream, using thematic analysis, explored the perspectives of general practitioners (GPs) and people with lived experience of depression around relapse risk and prevention in practice. Results The systematic review identified eleven models; none could currently be implemented in a primary care setting. The prognostic model developed in this study had inadequate predictive performance on internal validation (C-statistic 0.60; calibration slope 0.81). I carried out twenty-two semi-structured interviews with GPs and twenty-three with people with lived experience of depression. People with lived experience of depression and GPs reflected that a discussion around relapse would be useful but was not routinely offered. Both participant groups felt there would be benefits to relapse prevention for depression being embedded within primary care. Conclusions We are currently unable to accurately predict an individual’s risk of depression relapse. The longer-term care of people with depression in general practice could be improved by enabling continuity of care, increased consistency and clarity around follow-up arrangements, and focussed discussions around relapse risk and prevention. Scalable, brief relapse prevention interventions are needed, which would require policy change and additional resource. We need to better understand existing interventions and barriers to implementation in practice

    The 26th Annual Boston University Undergraduate Research (UROP) Abstracts

    Full text link
    The file is available to be viewed by anyone in the BU community. To view the file, click on "Login" or the Person icon top-right with your BU Kerberos password. You will then be able to see an option to View.Abstracts for the 2023 UROP Symposium, held at Boston University on October 20, 2023 at GSU Metcalf Ballroom. Cover and logo design by Morgan Danna. Booklet compiled by Molly Power

    Cyber-Human Systems, Space Technologies, and Threats

    Get PDF
    CYBER-HUMAN SYSTEMS, SPACE TECHNOLOGIES, AND THREATS is our eighth textbook in a series covering the world of UASs / CUAS/ UUVs / SPACE. Other textbooks in our series are Space Systems Emerging Technologies and Operations; Drone Delivery of CBNRECy – DEW Weapons: Emerging Threats of Mini-Weapons of Mass Destruction and Disruption (WMDD); Disruptive Technologies with applications in Airline, Marine, Defense Industries; Unmanned Vehicle Systems & Operations On Air, Sea, Land; Counter Unmanned Aircraft Systems Technologies and Operations; Unmanned Aircraft Systems in the Cyber Domain: Protecting USA’s Advanced Air Assets, 2nd edition; and Unmanned Aircraft Systems (UAS) in the Cyber Domain Protecting USA’s Advanced Air Assets, 1st edition. Our previous seven titles have received considerable global recognition in the field. (Nichols & Carter, 2022) (Nichols, et al., 2021) (Nichols R. K., et al., 2020) (Nichols R. , et al., 2020) (Nichols R. , et al., 2019) (Nichols R. K., 2018) (Nichols R. K., et al., 2022)https://newprairiepress.org/ebooks/1052/thumbnail.jp
    • …
    corecore