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Artificial intelligence is dramatically reshaping scientific research and is coming to play an essential role in scientific and technological development by enhancing and accelerating discovery across multiple fields. This book dives into the interplay between artificial intelligence and the quantum sciences; the outcome of a collaborative effort from world-leading experts. After presenting the key concepts and foundations of machine learning, a subfield of artificial intelligence, its applications in quantum chemistry and physics are presented in an accessible way, enabling readers to engage with emerging literature on machine learning in science. By examining its state-of-the-art applications, readers will discover how machine learning is being applied within their own field and appreciate its broader impact on science and technology. This book is accessible to undergraduates and more advanced readers from physics, chemistry, engineering, and computer science. Online resources include Jupyter notebooks to expand and develop upon key topics introduced in the book.
Combining two successful texts, Clinical Fluid Therapy in the Perioperative Setting, 2nd edition and Perioperative Hemodynamic Monitoring and Goal Directed Therapy, this revised volume provides a guide to fluid management and hemodynamic therapy for the perioperative practitioner. The book begins with an up-to-date overview of the basics before then exploring most of the current and controversial topics within hemodynamic monitoring and fluid therapy. This is followed by a section on practical use which explores hemodynamic and fluid therapy in various types of surgery and patient conditions. The book closes with a discussion of the future concepts in fluid and hemodynamic therapy ranging from microcirculation, to closed-loop and mobiles technologies. With contributions from the world's leading experts, chapters guide the reader in the application of fluid and hemodynamic therapy in all aspects of perioperative patient care. A valuable resource for those involved in perioperative patient management, including anaesthesiologists, intensivists, and surgeons.
Advance consent could allow individuals at high risk of stroke to provide consent before they might become eligible for enrollment in acute stroke trials. This survey explores the acceptability of this novel technique to Canadian Research Ethics Board (REB) chairs that review acute stroke trials. Responses from 15 REB chairs showed that majority of respondents expressed comfort approving studies that adopt advance consent. There was no clear preference for advance consent over deferral of consent, although respondents expressed significant concern with broad rather than trial-specific advance consent. These findings shed light on the acceptability of advance consent to Canadian ethics regulators.
Advance consent presents a potential solution to the challenge of obtaining informed consent for participation in acute stroke trials. Clinicians in stroke prevention clinics are uniquely positioned to identify and seek consent from potential stroke trial participants. To assess the acceptability of advance consent to Canadian stroke clinic physicians, we performed an online survey. We obtained 58 respondents (response rate 35%): the vast majority (82%) expressed comfort with obtaining advance consent and 92% felt that doing so would not be a significant disruption to clinic workflow. These results support further study of advance consent for acute stroke trials.
The Hierarchical Taxonomy of Psychopathology (HiTOP) has emerged out of the quantitative approach to psychiatric nosology. This approach identifies psychopathology constructs based on patterns of co-variation among signs and symptoms. The initial HiTOP model, which was published in 2017, is based on a large literature that spans decades of research. HiTOP is a living model that undergoes revision as new data become available. Here we discuss advantages and practical considerations of using this system in psychiatric practice and research. We especially highlight limitations of HiTOP and ongoing efforts to address them. We describe differences and similarities between HiTOP and existing diagnostic systems. Next, we review the types of evidence that informed development of HiTOP, including populations in which it has been studied and data on its validity. The paper also describes how HiTOP can facilitate research on genetic and environmental causes of psychopathology as well as the search for neurobiologic mechanisms and novel treatments. Furthermore, we consider implications for public health programs and prevention of mental disorders. We also review data on clinical utility and illustrate clinical application of HiTOP. Importantly, the model is based on measures and practices that are already used widely in clinical settings. HiTOP offers a way to organize and formalize these techniques. This model already can contribute to progress in psychiatry and complement traditional nosologies. Moreover, HiTOP seeks to facilitate research on linkages between phenotypes and biological processes, which may enable construction of a system that encompasses both biomarkers and precise clinical description.
Response to lithium in patients with bipolar disorder is associated with clinical and transdiagnostic genetic factors. The predictive combination of these variables might help clinicians better predict which patients will respond to lithium treatment.
Aims
To use a combination of transdiagnostic genetic and clinical factors to predict lithium response in patients with bipolar disorder.
Method
This study utilised genetic and clinical data (n = 1034) collected as part of the International Consortium on Lithium Genetics (ConLi+Gen) project. Polygenic risk scores (PRS) were computed for schizophrenia and major depressive disorder, and then combined with clinical variables using a cross-validated machine-learning regression approach. Unimodal, multimodal and genetically stratified models were trained and validated using ridge, elastic net and random forest regression on 692 patients with bipolar disorder from ten study sites using leave-site-out cross-validation. All models were then tested on an independent test set of 342 patients. The best performing models were then tested in a classification framework.
Results
The best performing linear model explained 5.1% (P = 0.0001) of variance in lithium response and was composed of clinical variables, PRS variables and interaction terms between them. The best performing non-linear model used only clinical variables and explained 8.1% (P = 0.0001) of variance in lithium response. A priori genomic stratification improved non-linear model performance to 13.7% (P = 0.0001) and improved the binary classification of lithium response. This model stratified patients based on their meta-polygenic loadings for major depressive disorder and schizophrenia and was then trained using clinical data.
Conclusions
Using PRS to first stratify patients genetically and then train machine-learning models with clinical predictors led to large improvements in lithium response prediction. When used with other PRS and biological markers in the future this approach may help inform which patients are most likely to respond to lithium treatment.
White Guinea yam (Dioscorea rotundata Poir.) is indigenous to West Africa, a region that harbours the crop's tremendous landrace diversity. The knowledge and understanding of local cultivars’ genetic diversity are essential for properly managing genetic resources, conservation, sustainable use and their improvement through breeding. This study aimed to dissect phenotypic and molecular diversity of white yam cultivars from Benin using agro-morphological and single nucleotide polymorphism (SNP) markers. Eighty-eight Beninese white Guinea yam cultivars collected through a countrywide ethnobotanical survey were phenotyped with 53 traits and genotyped with 9725 DArT-SNP. Multivariate analysis using phenotypic traits revealed 30 traits as most discriminative and explained up to 80.78% of cultivars’ phenotypic variation. Assessment of diversity indices such as Shannon–Wiener (H′), inverse Shannon (H.B.), Simpson's (λ) index and Pilou evenness (J) based molecular and phenotypic data depicted a moderate genetic diversity in Beninese white Guinea yam cultivars. Genetic differentiation of cultivars among country production zones was low due to the high exchange of planting materials among farmers of different regions. However, there was high genetic diversity within regions. Hierarchical clusters (HCs) on phenotypic data revealed the presence of two groups while HCs based on the SNP markers and the combined analysis identified three genetic groups. Our result provided valuable insights into the Beninese white Guinea yam diversity for its proper conservation and improvement through breeding.