The dark universe contains matter and energy unidentifiable with current physical models, accounting for 95% of all the matter and energetic equivalent in the universe. The enormous surplus brings up daunting enigmas, such as the cosmological constant problem and the apparent distortions in the dynamics of deep space, and so coming to grips with the invisible universe has become a scientific imperative. This book addresses this need, reckoning that no cogent physical model of the dark universe can be implemented without first addressing the metaphysical hurdles along the way. The foremost problem is identifying the topology of the universe which, as argued in the book, is highly relevant to unveil the secrets of the dark universe. Artificial Intelligence (AI) is a valuable tool in this effort since it can reconcile conflicting data from deep space with the extant laws of physics by building models to decipher the dark universe. This book explores the applications of AI and how it can be used to embark on a metaphysical quest to identify the topology of the universe as a prerequisite to implement a physical model of the dark sector that enables a meaningful extrapolation into the visibile sector. The book is intended for a broad readership, but a background in college-level physics and computer science is essential. The book will be a valuable guide for graduate students as well as researchers in physics, astrophysics, and computer science focusing on AI applications to elucidate the nature of the dark universe. Key Features: · Provides readers with an intellectual toolbox to understand physical arguments on dark matter and energy. · Up to date with the latest cutting-edge research. · Authored by an expert on artificial intelligence and mathematical physics.
This book explores the possibility of the use of artificial intelligence (AI) to solve one of the cosmos’ biggest mysteries: the nature of undetectable forms of matter, namely dark matter and dark energy, which make up 95% of the universe. The book describes the outcome of this quest in terms of an entangled ur-universe that admits no observer, and incorporates an extra dimension to encode space-time as a latent manifold. A cosmic engine fueled by dark energy that maintains the topology of the universe during its expansion, involving autocatalytic vacuum creation, is identified. The physical picture of the cosmos presented in the book paves the way for a solution to the cosmological constant problem and provides a cogent explanation for the huge gap between the predicted and measured values that has troubled physicists for decades.
The book focuses on the aqueous interface of biomolecules, a vital yet overlooked area of biophysical research. Most biological phenomena cannot be fully understood at the molecular level without considering interfacial behavior. The author presents conceptual advances in molecular biophysics that herald the advent of a new discipline, epistructural biology, centered on the interactions of water and bio molecular structures across the interface. The author introduces powerful theoretical and computational resources in order to address fundamental topics such as protein folding, the physico-chemical basis of enzyme catalysis and protein associations. On the basis of this information, a multi-disciplinary approach is used to engineer therapeutic drugs and to allow substantive advances in targeted molecular medicine. This book will be of interest to scientists, students and practitioners in the fields of chemistry, biophysics and biomedical engineering.
This book focuses primarily on the role of interfacial forces in understanding biological phenomena at the molecular scale. By providing a suitable statistical mechanical apparatus to handle the biomolecular interface, the book becomes uniquely positioned to address core problems in molecular biophysics. It highlights the importance of interfacial tension in delineating a solution to the protein folding problem, in unravelling the physico-chemical basis of enzyme catalysis and protein associations, and in rationally designing molecular targeted therapies. Thus grounded in fundamental science, the book develops a powerful technological platform for drug discovery, while it is set to inspire scientists at any level in their careers determined to address the major challenges in molecular biophysics. The acknowledgment of how exquisitely the structure and dynamics of proteins and their aqueous environment are related attests to the overdue recognition that biomolecular phenomena cannot be effectively understood without dealing with interfacial behaviour. There is an urge to grasp how biologically relevant behaviour is shaped by the structuring of biomolecular interfaces and how interfacial tension affects the molecular events that take place in the cell. This book squarely addresses these needs from a physicist perspective. The book may serve as a monograph for practitioners and, alternatively, as an advanced textbook. Fruitful reading requires a background in physical chemistry and some basics in biophysics. The selected problems at the end of the chapters and the progression in conceptual difficulty make it a suitable textbook for a graduate level course or an elective course for seniors majoring in chemistry, physics, biomedical engineering or related disciplines.
This book explores quantitative aspects of protein biophysics and attempts to delineate certain rules of molecular behavior that make atomic scale objects behave in a digital way. This book will help readers to understand how certain biological systems involving proteins function as digital information systems despite the fact that underlying processes are analog in nature. The in-depth explanation of proteins from a quantitative point of view and the variety of level of exercises (including physical experiments) at the end of each chapter will appeal to graduate and senior undergraduate students in mathematics, computer science, mechanical engineering, and physics, wanting to learn about the biophysics of proteins. L. Ridgway Scott has been Professor of Computer Science and of Mathematics at the University of Chicago since 1998, and the Louis Block Professor since 2001. He obtained a B.S. degree (Magna Cum Laude) from Tulane University in 1969 and a PhD degree in Mathematics from the Massachusetts Institute of Technology in 1973. Professor Scott has published over 130 papers and three books, extending over biophysics, parallel computing and fundamental computing aspects of structural mechanics, fluid dynamics, nuclear engineering, and computational chemistry. Ariel Fernández (born Ariel Fernández Stigliano) is an Argentinian-American physical chemist and mathematician. He obtained his Ph. D. degree in Chemical Physics from Yale University and held the Karl F. Hasselmann Endowed Chair Professorship in Bioengineering at Rice University. He is currently involved in research and entrepreneurial activities at various consultancy firms. Ariel Fernández authored three books on translational medicine and biophysics, and published 360 papers in professional journals. He holds two patents in the field of biotechnology.
As we prod the cosmos at very large scales, basic tenets of physics seem to crumble under the weight of contradicting evidence. This book helps mitigate the crisis. It resorts to artificial intelligence (AI) for answers and describes the outcome of this quest in terms of an ur-universe, a quintessential compact multiply connected space that incorporates a fifth dimension to encode space-time as a latent manifold. In some ways, AI is bolder than humans because the huge corpus of knowledge, starting with the prodigious Standard Model (SM) of particle physics, poses almost no burden to its conjecture-framing processes. Why not feed AI with the SM enriched by the troubling cosmological phenomenology on dark matter and dark energy and see where AI takes us vis-à-vis reconciling the conflicting data with the laws of physics? This is precisely the intellectual adventure described in this book and – to the best of our knowledge – in no other book on the shelf. As the reader will discover, many AI conjectures and validations ultimately make a lot of sense, even if their boldness does not feel altogether "human" yet. This book is written for a broad readership. Prerequisites are minimal, but a background in college math/physics/computer science is desirable. This book does not merely describe what is known about dark matter and dark energy but also provides readers with intellectual tools to engage in a quest for the deepest cosmological mystery.
In spite of the enticing promises of the post-genomic era, the pharmaceutical world is in a state of disarray. Drug discovery seems now riskier and more uncertain than ever. Thus, projects get routinely terminated in mid-stage clinical trials, new targets are getting harder to find, and successful therapeutic agents are often recalled as unanticipated side effects are discovered. Exploiting the huge output of genomic studies to make safer drugs has proven to be much more difficult than anticipated. More than ever, the lead in the pharmaceutical industry depends on the ability to harness innovative research, and this type of innovation can only come from one source: fundamental knowledge. This book squarely addresses this crucial problem since it introduces fundamental discoveries in basic biomolecular research that hold potential to broaden the technological base of the pharmaceutical industry. The book takes a fresh and fundamental look at the problem of how to design an effective drug with controlled specificity. Since the novel transformative concepts are unfamiliar to most practitioners, the first part of this book explains matters very carefully starting from a fairly elementary physico-chemical level. The second part of the book is devoted to practical applications, aiming at nothing less than a paradigm shift in drug design. This book is addressed to scientists working at the cutting edge of research in the pharmaceutical industry, but the material is at the same time accessible to senior undergraduates or graduate students interested in drug discovery and molecular design.
The leveraging of artificial intelligence (AI) for model discovery in dynamical systems is cross-fertilizing and revolutionizing both disciplines, heralding a new era of data-driven science. This book is placed at the forefront of this endeavor, taking model discovery to the next level. Dealing with artificial intelligence, this book delineates AI’s role in model discovery for dynamical systems. With the implementation of topological methods to construct metamodels, it engages with levels of complexity and multiscale hierarchies hitherto considered off limits for data science. Key Features: Introduces new and advanced methods of model discovery for time series data using artificial intelligence Implements topological approaches to distill "machine-intuitive" models from complex dynamics data Introduces a new paradigm for a parsimonious model of a dynamical system without resorting to differential equations Heralds a new era in data-driven science and engineering based on the operational concept of "computational intuition" Intended for graduate students, researchers, and practitioners interested in dynamical systems empowered by AI or machine learning and in their biological, engineering, and biomedical applications, this book will represent a significant educational resource for people engaged in AI-related cross-disciplinary projects.
In the era of big biomedical data, there are many ways in which artificial intelligence (AI) is likely to broaden the technological base of the pharmaceutical industry. Cheminformatic applications of AI involving the parsing of chemical space are already being implemented to infer compound properties and activity. By contrast, dynamic aspects of the design of drug/target interfaces have received little attention due to the inherent difficulties in dealing with physical phenomena that often do not conform to simplifying views.This book focuses precisely on dynamic drug/target interfaces and argues that the true game change in pharmaceutical discovery will come as AI is enabled to solve core problems in molecular biophysics that are intimately related to rational drug design and drug discovery.Here are a few examples to convey the flavor of our quest: How do we therapeutically impair a dysfunctional protein with unknown structure or regulation but known to be a culprit of disease? In regards to SARS-CoV-2, what is the structural impact of a dominant mutation?, how does the structure change translate into a fitness advantage?, what new therapeutic opportunity arises? How do we extend molecular dynamics simulations to realistic timescales, to capture the rare events associated with drug targeting in vivo? How do we control specificity in drug design to selectively remove side effects? This is the type of problems, directly related to the understanding of drug/target interfaces, that the book squarely addresses by leveraging a comprehensive AI-empowered approach.
The leveraging of artificial intelligence (AI) for model discovery in dynamical systems is cross-fertilizing and revolutionizing both disciplines, heralding a new era of data-driven science. This book is placed at the forefront of this endeavor, taking model discovery to the next level. Dealing with artificial intelligence, this book delineates AI’s role in model discovery for dynamical systems. With the implementation of topological methods to construct metamodels, it engages with levels of complexity and multiscale hierarchies hitherto considered off limits for data science. Key Features: Introduces new and advanced methods of model discovery for time series data using artificial intelligence Implements topological approaches to distill "machine-intuitive" models from complex dynamics data Introduces a new paradigm for a parsimonious model of a dynamical system without resorting to differential equations Heralds a new era in data-driven science and engineering based on the operational concept of "computational intuition" Intended for graduate students, researchers, and practitioners interested in dynamical systems empowered by AI or machine learning and in their biological, engineering, and biomedical applications, this book will represent a significant educational resource for people engaged in AI-related cross-disciplinary projects.
In the era of big biomedical data, there are many ways in which artificial intelligence (AI) is likely to broaden the technological base of the pharmaceutical industry. Cheminformatic applications of AI involving the parsing of chemical space are already being implemented to infer compound properties and activity. By contrast, dynamic aspects of the design of drug/target interfaces have received little attention due to the inherent difficulties in dealing with physical phenomena that often do not conform to simplifying views.This book focuses precisely on dynamic drug/target interfaces and argues that the true game change in pharmaceutical discovery will come as AI is enabled to solve core problems in molecular biophysics that are intimately related to rational drug design and drug discovery.Here are a few examples to convey the flavor of our quest: How do we therapeutically impair a dysfunctional protein with unknown structure or regulation but known to be a culprit of disease? In regards to SARS-CoV-2, what is the structural impact of a dominant mutation?, how does the structure change translate into a fitness advantage?, what new therapeutic opportunity arises? How do we extend molecular dynamics simulations to realistic timescales, to capture the rare events associated with drug targeting in vivo? How do we control specificity in drug design to selectively remove side effects? This is the type of problems, directly related to the understanding of drug/target interfaces, that the book squarely addresses by leveraging a comprehensive AI-empowered approach.
This book focuses primarily on the role of interfacial forces in understanding biological phenomena at the molecular scale. By providing a suitable statistical mechanical apparatus to handle the biomolecular interface, the book becomes uniquely positioned to address core problems in molecular biophysics. It highlights the importance of interfacial tension in delineating a solution to the protein folding problem, in unravelling the physico-chemical basis of enzyme catalysis and protein associations, and in rationally designing molecular targeted therapies. Thus grounded in fundamental science, the book develops a powerful technological platform for drug discovery, while it is set to inspire scientists at any level in their careers determined to address the major challenges in molecular biophysics. The acknowledgment of how exquisitely the structure and dynamics of proteins and their aqueous environment are related attests to the overdue recognition that biomolecular phenomena cannot be effectively understood without dealing with interfacial behaviour. There is an urge to grasp how biologically relevant behaviour is shaped by the structuring of biomolecular interfaces and how interfacial tension affects the molecular events that take place in the cell. This book squarely addresses these needs from a physicist perspective. The book may serve as a monograph for practitioners and, alternatively, as an advanced textbook. Fruitful reading requires a background in physical chemistry and some basics in biophysics. The selected problems at the end of the chapters and the progression in conceptual difficulty make it a suitable textbook for a graduate level course or an elective course for seniors majoring in chemistry, physics, biomedical engineering or related disciplines.
In spite of the enticing promises of the post-genomic era, the pharmaceutical world is in a state of disarray. Drug discovery seems now riskier and more uncertain than ever. Thus, projects get routinely terminated in mid-stage clinical trials, new targets are getting harder to find, and successful therapeutic agents are often recalled as unanticipated side effects are discovered. Exploiting the huge output of genomic studies to make safer drugs has proven to be much more difficult than anticipated. More than ever, the lead in the pharmaceutical industry depends on the ability to harness innovative research, and this type of innovation can only come from one source: fundamental knowledge. This book squarely addresses this crucial problem since it introduces fundamental discoveries in basic biomolecular research that hold potential to broaden the technological base of the pharmaceutical industry. The book takes a fresh and fundamental look at the problem of how to design an effective drug with controlled specificity. Since the novel transformative concepts are unfamiliar to most practitioners, the first part of this book explains matters very carefully starting from a fairly elementary physico-chemical level. The second part of the book is devoted to practical applications, aiming at nothing less than a paradigm shift in drug design. This book is addressed to scientists working at the cutting edge of research in the pharmaceutical industry, but the material is at the same time accessible to senior undergraduates or graduate students interested in drug discovery and molecular design.
This book explores the possibility of the use of artificial intelligence (AI) to solve one of the cosmos’ biggest mysteries: the nature of undetectable forms of matter, namely dark matter and dark energy, which make up 95% of the universe. The book describes the outcome of this quest in terms of an entangled ur-universe that admits no observer, and incorporates an extra dimension to encode space-time as a latent manifold. A cosmic engine fueled by dark energy that maintains the topology of the universe during its expansion, involving autocatalytic vacuum creation, is identified. The physical picture of the cosmos presented in the book paves the way for a solution to the cosmological constant problem and provides a cogent explanation for the huge gap between the predicted and measured values that has troubled physicists for decades.
The book focuses on the aqueous interface of biomolecules, a vital yet overlooked area of biophysical research. Most biological phenomena cannot be fully understood at the molecular level without considering interfacial behavior. The author presents conceptual advances in molecular biophysics that herald the advent of a new discipline, epistructural biology, centered on the interactions of water and bio molecular structures across the interface. The author introduces powerful theoretical and computational resources in order to address fundamental topics such as protein folding, the physico-chemical basis of enzyme catalysis and protein associations. On the basis of this information, a multi-disciplinary approach is used to engineer therapeutic drugs and to allow substantive advances in targeted molecular medicine. This book will be of interest to scientists, students and practitioners in the fields of chemistry, biophysics and biomedical engineering.
The dark universe contains matter and energy unidentifiable with current physical models, accounting for 95% of all the matter and energetic equivalent in the universe. The enormous surplus brings up daunting enigmas, such as the cosmological constant problem and the apparent distortions in the dynamics of deep space, and so coming to grips with the invisible universe has become a scientific imperative. This book addresses this need, reckoning that no cogent physical model of the dark universe can be implemented without first addressing the metaphysical hurdles along the way. The foremost problem is identifying the topology of the universe which, as argued in the book, is highly relevant to unveil the secrets of the dark universe. Artificial Intelligence (AI) is a valuable tool in this effort since it can reconcile conflicting data from deep space with the extant laws of physics by building models to decipher the dark universe. This book explores the applications of AI and how it can be used to embark on a metaphysical quest to identify the topology of the universe as a prerequisite to implement a physical model of the dark sector that enables a meaningful extrapolation into the visibile sector. The book is intended for a broad readership, but a background in college-level physics and computer science is essential. The book will be a valuable guide for graduate students as well as researchers in physics, astrophysics, and computer science focusing on AI applications to elucidate the nature of the dark universe. Key Features: · Provides readers with an intellectual toolbox to understand physical arguments on dark matter and energy. · Up to date with the latest cutting-edge research. · Authored by an expert on artificial intelligence and mathematical physics.
As we prod the cosmos at very large scales, basic tenets of physics seem to crumble under the weight of contradicting evidence. This book helps mitigate the crisis. It resorts to artificial intelligence (AI) for answers and describes the outcome of this quest in terms of an ur-universe, a quintessential compact multiply connected space that incorporates a fifth dimension to encode space-time as a latent manifold. In some ways, AI is bolder than humans because the huge corpus of knowledge, starting with the prodigious Standard Model (SM) of particle physics, poses almost no burden to its conjecture-framing processes. Why not feed AI with the SM enriched by the troubling cosmological phenomenology on dark matter and dark energy and see where AI takes us vis-à-vis reconciling the conflicting data with the laws of physics? This is precisely the intellectual adventure described in this book and – to the best of our knowledge – in no other book on the shelf. As the reader will discover, many AI conjectures and validations ultimately make a lot of sense, even if their boldness does not feel altogether "human" yet. This book is written for a broad readership. Prerequisites are minimal, but a background in college math/physics/computer science is desirable. This book does not merely describe what is known about dark matter and dark energy but also provides readers with intellectual tools to engage in a quest for the deepest cosmological mystery.
In Children of Facundo Ariel de la Fuente examines postindependence Argentinian instability and political struggle from the perspective of the rural lower classes. As the first comprehensive regional study to explore nineteenth-century society, culture, and politics in the Argentine interior—where more than 50 percent of the population lived at the time—the book departs from the predominant Buenos Aires-centered historiography to analyze this crucial period in the processes of state- and nation-building. La Rioja, a province in the northwest section of the country, was the land of the caudillos immortalized by Domingo F. Sarmiento, particularly in his foundational and controversial book Facundo. De la Fuente focuses on the repeated rebellions in this district during the 1860s, when Federalist caudillos and their followers, the gauchos, rose up against the new Unitarian government. In this social and cultural analysis, de la Fuente argues that the conflict was not a factional struggle between two ideologically identical sectors of the elite, as commonly depicted. Instead, he believes, the struggle should be seen from the perspective of the lower-class gauchos, for whom Unitarianism and Federalism were highly differentiated party identities that represented different experiences during the nineteenth century. To reconstruct this rural political culture de la Fuente relies on sources that heretofore have been little used in the study of nineteenth-century Latin American politics, most notably a rich folklore collection of popular political songs, folktales, testimonies, and superstitions passed down by old gauchos who had been witnesses or protagonists of the rebellions. Criminal trial records, private diaries, and land censuses add to the originality of de la Fuente’s study, while also providing a new perspective on Sarmiento’s works, including the classic Facundo. This book will interest those specializing in Latin American history, literature, politics, and rural issues.
This book explores quantitative aspects of protein biophysics and attempts to delineate certain rules of molecular behavior that make atomic scale objects behave in a digital way. This book will help readers to understand how certain biological systems involving proteins function as digital information systems despite the fact that underlying processes are analog in nature. The in-depth explanation of proteins from a quantitative point of view and the variety of level of exercises (including physical experiments) at the end of each chapter will appeal to graduate and senior undergraduate students in mathematics, computer science, mechanical engineering, and physics, wanting to learn about the biophysics of proteins. L. Ridgway Scott has been Professor of Computer Science and of Mathematics at the University of Chicago since 1998, and the Louis Block Professor since 2001. He obtained a B.S. degree (Magna Cum Laude) from Tulane University in 1969 and a PhD degree in Mathematics from the Massachusetts Institute of Technology in 1973. Professor Scott has published over 130 papers and three books, extending over biophysics, parallel computing and fundamental computing aspects of structural mechanics, fluid dynamics, nuclear engineering, and computational chemistry. Ariel Fernández (born Ariel Fernández Stigliano) is an Argentinian-American physical chemist and mathematician. He obtained his Ph. D. degree in Chemical Physics from Yale University and held the Karl F. Hasselmann Endowed Chair Professorship in Bioengineering at Rice University. He is currently involved in research and entrepreneurial activities at various consultancy firms. Ariel Fernández authored three books on translational medicine and biophysics, and published 360 papers in professional journals. He holds two patents in the field of biotechnology.
Vividly recasting Cuba's politics in the 1930s as transnational, Ariel Mae Lambe has produced an unprecendented reimagining of Cuban activism during an era previously regarded as a lengthy, defeated lull. In this period, many Cuban activists began to look at their fight against strongman rule and neocolonial control at home as part of the international antifascism movement that exploded with the Spanish Civil War. Frustrated by multiple domestic setbacks, including Colonel Fulgencio Batista's violent crushing of a massive general strike, activists found strength in the face of repression by refusing to view their political goals as confined to the island. As individuals and in groups, Cubans from diverse backgrounds and political stances self-identified as antifascists and moved, both physically and symbolically, across borders and oceans, cultivating networks and building solidarity for a New Spain and a New Cuba. They believed that it was through these ostensibly foreign fights that they would achieve economic and social progress for their nation. Indeed, Cuban antifascism was such a strong movement, Lambe argues, that it helps to explain the surprisingly progressive turn that Batista and the Cuban government took at the end of the decade, including the establishment of a new constitution and presidential elections.
Textbook of Assisted Reproductive Techniques has become a classic comprehensive reference for the whole team at the IVF clinic. The fourth edition comes more conveniently as a set of two separate volumes, one for laboratory aspects and the other for clinical applications. The text has been extensively revised, with the addition of several important new contributions on laboratory aspects including developing techniques such as PICSI, IMSI, and time-lapse imaging. The second volume focuses on clinical applications and includes new chapters on lifestyle factors, tailored ovarian stimulation, frozen-thawed embryo transfer, viral disease, and religious perspectives. As before, methods, protocols, and techniques of choice are presented by eminent international experts. The two volume set includes: ■ Volume One - Laboratory Perspectives ■ Volume Two - Clinical Perspectives
Over the last quarter century, no other city like Miami has rapidly transformed into a global city. The Global Edge charts the social tensions and unexpected consequences of this remarkable process of change. Acting as a follow-up to the highly successful City on the Edge, The Global Edge examines Miami in the context of globalization and scrutinizes its newfound place as a major international city. Written by two well-known scholars in the field, the book examines Miami’s rise as a finance and banking center and the simultaneous emergence of a highly diverse but contentious ethnic mosaic. The Global Edge serves as a case study of Miami’s present cultural, economic, and political transformation, and describes how its future course can provide key lessons for other metropolitan areas throughout the world.
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