Jules J. Berman – författare

Principles of Big Data helps readers avoid the common mistakes that endanger all Big Data projects. By stressing simple, fundamental concepts, this book teaches readers how to organize large volumes of complex data, and how to achieve data permanence when the content of the data is constantly changing. General methods for data verification and validation, as specifically applied to Big Data resources, are stressed throughout the book. The book demonstrates how adept analysts can find relationships among data objects held in disparate Big Data resources, when the data objects are endowed with semantic support (i.e., organized in classes of uniquely identified data objects). Readers will learn how their data can be integrated with data from other resources, and how the data extracted from Big Data resources can be used for purposes beyond those imagined by the data creators.

Learn general methods for specifying Big Data in a way that is understandable to humans and to computers Avoid the pitfalls in Big Data design and analysis Understand how to create and use Big Data safely and responsibly with a set of laws, regulations and ethical standards that apply to the acquisition, distribution and integration of Big Data resources

Principles of Big Data helps readers avoid the common mistakes that endanger all Big Data projects. By stressing simple, fundamental concepts, this book teaches readers how to organize large volumes of complex data, and how to achieve data permanence when the content of the data is constantly changing. General methods for data verification and validation, as specifically applied to Big Data resources, are stressed throughout the book. The book demonstrates how adept analysts can find relationships among data objects held in disparate Big Data resources, when the data objects are endowed with semantic support (i.e., organized in classes of uniquely identified data objects). Readers will learn how their data can be integrated with data from other resources, and how the data extracted from Big Data resources can be used for purposes beyond those imagined by the data creators.- Learn general methods for specifying Big Data in a way that is understandable to humans and to computers- Avoid the pitfalls in Big Data design and analysis- Understand how to create and use Big Data safely and responsibly with a set of laws, regulations and ethical standards that apply to the acquisition, distribution and integration of Big Data resources

In the past few decades, there have been great advances in the phylogenetic classification of infectious diseases of man. Taxonomic Guide to Infectious Diseases organizes this information into a standard biological classification and provides a short, clinically-oriented description of every genus (class) of infectious organism. It covers an overview of modern taxonomy, including a description of the kingdoms of life and the evolutionary principles underlying the class hierarchy, and each following chapter will describe one phylum and the genera that contain infectious species. Taxonomic Guide to Infectious Diseases is written in an engaging, narrative style, providing the reader with an easy to digest yet clinically-oriented story of the pathogenic features of each genus. Designed for researchers, clinicians and students of infectious diseases, medical microbiology and pathology. Offers genus-by-genus classification of infectious diseases along with short, clinically-oriented descriptions of each genus Presents comprehensive lists of infectious species for each genera and identifies diseases caused by each species Compiled and written by a well-known pathologist with extensive experience in diagnosing human infectious diseases

In the past few decades, there have been great advances in the phylogenetic classification of infectious diseases of man. Taxonomic Guide to Infectious Diseases organizes this information into a standard biological classification and provides a short, clinically-oriented description of every genus (class) of infectious organism. It covers an overview of modern taxonomy, including a description of the kingdoms of life and the evolutionary principles underlying the class hierarchy, and each following chapter will describe one phylum and the genera that contain infectious species. Taxonomic Guide to Infectious Diseases is written in an engaging, narrative style, providing the reader with an easy to digest yet clinically-oriented story of the pathogenic features of each genus. Designed for researchers, clinicians and students of infectious diseases, medical microbiology and pathology.- Offers genus-by-genus classification of infectious diseases along with short, clinically-oriented descriptions of each genus- Presents comprehensive lists of infectious species for each genera and identifies diseases caused by each species- Compiled and written by a well-known pathologist with extensive experience in diagnosing human infectious diseases

Rare Diseases and Orphan Drugs shows that much of what we now know about common diseases has been achieved by studying rare diseases. It proposes that future advances in the prevention, diagnosis, and treatment of common diseases will come as a consequence of our accelerating progress in the field of rare diseases.

Understanding the complex steps in the development of common diseases, such as cancer, cardiovascular disease, and metabolic diseases, has proven a difficult problem. Rare diseases, however, are often caused by aberrations of a single gene. In rare diseases, we may study how specific genetic defects can trigger a series of events that lead to the expression of a particular disease. Often, the disease process manifested in a certain rare disease is strikingly similar to the disease process observed in a common disease.

This work ties the lessons learned about rare diseases to our understanding of common ones. Chapters covering the number of common diseases are minimized, while rare diseases are introduced as single diseases or as members of diseases classes. After reading this book, readers will appreciate how further research into the rare diseases may lead to new methods for preventing, diagnosing, and treating all diseases, rare or common.

Makes rare diseases relevant to clinicians and researchers by tying lessons learned about the rare diseases to our understanding of the common diseases Stresses basic pathologic mechanisms that account for human disease (e.g., disorders of cell development, replication, maintenance, function and structure), that can be understood without prior training in pathology Discusses advanced concepts in molecular biology and genetics in a simple, functional context appropriate for medical trainees and new researchers Offers insights into how further research into rare diseases may lead to new methods for preventing, diagnosing, and treating all diseases

Rare Diseases and Orphan Drugs shows that much of what we now know about common diseases has been achieved by studying rare diseases. It proposes that future advances in the prevention, diagnosis, and treatment of common diseases will come as a consequence of our accelerating progress in the field of rare diseases.Understanding the complex steps in the development of common diseases, such as cancer, cardiovascular disease, and metabolic diseases, has proven a difficult problem. Rare diseases, however, are often caused by aberrations of a single gene. In rare diseases, we may study how specific genetic defects can trigger a series of events that lead to the expression of a particular disease. Often, the disease process manifested in a certain rare disease is strikingly similar to the disease process observed in a common disease.This work ties the lessons learned about rare diseases to our understanding of common ones. Chapters covering the number of common diseases are minimized, while rare diseases are introduced as single diseases or as members of diseases classes. After reading this book, readers will appreciate how further research into the rare diseases may lead to new methods for preventing, diagnosing, and treating all diseases, rare or common.- Makes rare diseases relevant to clinicians and researchers by tying lessons learned about the rare diseases to our understanding of the common diseases- Stresses basic pathologic mechanisms that account for human disease (e.g., disorders of cell development, replication, maintenance, function and structure), that can be understood without prior training in pathology- Discusses advanced concepts in molecular biology and genetics in a simple, functional context appropriate for medical trainees and new researchers- Offers insights into how further research into rare diseases may lead to new methods for preventing, diagnosing, and treating all diseases

Repurposing Legacy Data: Innovative Case Studies takes a look at how data scientists have re-purposed legacy data, whether their own, or legacy data that has been donated to the public domain.

Most of the data stored worldwide is legacy data-data created some time in the past, for a particular purpose, and left in obsolete formats. As with keepsakes in an attic, we retain this information thinking it may have value in the future, though we have no current use for it.

The case studies in this book, from such diverse fields as cosmology, quantum physics, high-energy physics, microbiology, psychiatry, medicine, and hospital administration, all serve to demonstrate how innovative people draw value from legacy data. By following the case examples, readers will learn how legacy data is restored, merged, and analyzed for purposes that were never imagined by the original data creators.

Discusses how combining existing data with other data sets of the same kind can produce an aggregate data set that serves to answer questions that could not be answered with any of the original data Presents a method for re-analyzing original data sets using alternate or improved methods that can provide outcomes more precise and reliable than those produced in the original analysis Explains how to integrate heterogeneous data sets for the purpose of answering questions or developing concepts that span several different scientific fields

Repurposing Legacy Data: Innovative Case Studies takes a look at how data scientists have re-purposed legacy data, whether their own, or legacy data that has been donated to the public domain.Most of the data stored worldwide is legacy data—data created some time in the past, for a particular purpose, and left in obsolete formats. As with keepsakes in an attic, we retain this information thinking it may have value in the future, though we have no current use for it.The case studies in this book, from such diverse fields as cosmology, quantum physics, high-energy physics, microbiology, psychiatry, medicine, and hospital administration, all serve to demonstrate how innovative people draw value from legacy data. By following the case examples, readers will learn how legacy data is restored, merged, and analyzed for purposes that were never imagined by the original data creators.- Discusses how combining existing data with other data sets of the same kind can produce an aggregate data set that serves to answer questions that could not be answered with any of the original data- Presents a method for re-analyzing original data sets using alternate or improved methods that can provide outcomes more precise and reliable than those produced in the original analysis- Explains how to integrate heterogeneous data sets for the purpose of answering questions or developing concepts that span several different scientific fields

Data Simplification: Taming Information With Open Source Tools addresses the simple fact that modern data is too big and complex to analyze in its native form. Data simplification is the process whereby large and complex data is rendered usable. Complex data must be simplified before it can be analyzed, but the process of data simplification is anything but simple, requiring a specialized set of skills and tools.

This book provides data scientists from every scientific discipline with the methods and tools to simplify their data for immediate analysis or long-term storage in a form that can be readily repurposed or integrated with other data.

Drawing upon years of practical experience, and using numerous examples and use cases, Jules Berman discusses the principles, methods, and tools that must be studied and mastered to achieve data simplification, open source tools, free utilities and snippets of code that can be reused and repurposed to simplify data, natural language processing and machine translation as a tool to simplify data, and data summarization and visualization and the role they play in making data useful for the end user.

Discusses data simplification principles, methods, and tools that must be studied and mastered Provides open source tools, free utilities, and snippets of code that can be reused and repurposed to simplify data Explains how to best utilize indexes to search, retrieve, and analyze textual data Shows the data scientist how to apply ontologies, classifications, classes, properties, and instances to data using tried and true methods

Data Simplification: Taming Information With Open Source Tools addresses the simple fact that modern data is too big and complex to analyze in its native form. Data simplification is the process whereby large and complex data is rendered usable. Complex data must be simplified before it can be analyzed, but the process of data simplification is anything but simple, requiring a specialized set of skills and tools.This book provides data scientists from every scientific discipline with the methods and tools to simplify their data for immediate analysis or long-term storage in a form that can be readily repurposed or integrated with other data.Drawing upon years of practical experience, and using numerous examples and use cases, Jules Berman discusses the principles, methods, and tools that must be studied and mastered to achieve data simplification, open source tools, free utilities and snippets of code that can be reused and repurposed to simplify data, natural language processing and machine translation as a tool to simplify data, and data summarization and visualization and the role they play in making data useful for the end user.- Discusses data simplification principles, methods, and tools that must be studied and mastered- Provides open source tools, free utilities, and snippets of code that can be reused and repurposed to simplify data- Explains how to best utilize indexes to search, retrieve, and analyze textual data- Shows the data scientist how to apply ontologies, classifications, classes, properties, and instances to data using tried and true methods

Despite what you may have read in the popular press and in social media, Precision Medicine is not devoted to finding unique treatments for individuals, based on analyzing their DNA. To the contrary, the goal of Precision Medicine is to find general treatments that are highly effective for large numbers of individuals who fall into precisely diagnosed groups.

We now know that every disease develops over time, through a sequence of defined biological steps, and that these steps may differ among individuals, based on genetic and environmental conditions. We are currently developing rational therapies and preventive measures, based on our precise understanding of the steps leading to the clinical expression of diseases.

Precision Medicine and the Reinvention of Human Disease explains the scientific breakthroughs that have changed the way that we understand diseases, and reveals how medical scientists are using this new knowledge to launch a medical revolution.

Clarifies the foundational concepts of Precision Medicine, distinguishing this field from its predecessors such as genomics, pharmacogenetics, and personalized medicine Gathers the chief conceptual advances in the fields of genetics, pathology, and bioinformatics, and synthesizes a coherent narrative for the field of Precision Medicine Delivers its message in plain language, and in a relaxed, conversational writing style, making it easy to understand the complex subject matter Guides the reader through a coherent and logical narrative, gradually providing expertise and skills along the way Covers the importance of data sharing in Precision Medicine, and the many data-related challenges that confront this fragile new field

Despite what you may have read in the popular press and in social media, Precision Medicine is not devoted to finding unique treatments for individuals, based on analyzing their DNA. To the contrary, the goal of Precision Medicine is to find general treatments that are highly effective for large numbers of individuals who fall into precisely diagnosed groups.We now know that every disease develops over time, through a sequence of defined biological steps, and that these steps may differ among individuals, based on genetic and environmental conditions. We are currently developing rational therapies and preventive measures, based on our precise understanding of the steps leading to the clinical expression of diseases.Precision Medicine and the Reinvention of Human Disease explains the scientific breakthroughs that have changed the way that we understand diseases, and reveals how medical scientists are using this new knowledge to launch a medical revolution.- Clarifies the foundational concepts of Precision Medicine, distinguishing this field from its predecessors such as genomics, pharmacogenetics, and personalized medicine- Gathers the chief conceptual advances in the fields of genetics, pathology, and bioinformatics, and synthesizes a coherent narrative for the field of Precision Medicine- Delivers its message in plain language, and in a relaxed, conversational writing style, making it easy to understand the complex subject matter- Guides the reader through a coherent and logical narrative, gradually providing expertise and skills along the way- Covers the importance of data sharing in Precision Medicine, and the many data-related challenges that confront this fragile new field

Principles and Practice of Big Data: Preparing, Sharing, and Analyzing Complex Information, Second Edition updates and expands on the first edition, bringing a set of techniques and algorithms that are tailored to Big Data projects. The book stresses the point that most data analyses conducted on large, complex data sets can be achieved without the use of specialized suites of software (e.g., Hadoop), and without expensive hardware (e.g., supercomputers). The core of every algorithm described in the book can be implemented in a few lines of code using just about any popular programming language (Python snippets are provided).

Through the use of new multiple examples, this edition demonstrates that if we understand our data, and if we know how to ask the right questions, we can learn a great deal from large and complex data collections. The book will assist students and professionals from all scientific backgrounds who are interested in stepping outside the traditional boundaries of their chosen academic disciplines.

Presents new methodologies that are widely applicable to just about any project involving large and complex datasets Offers readers informative new case studies across a range scientific and engineering disciplines Provides insights into semantics, identification, de-identification, vulnerabilities and regulatory/legal issues Utilizes a combination of pseudocode and very short snippets of Python code to show readers how they may develop their own projects without downloading or learning new software

Principles and Practice of Big Data: Preparing, Sharing, and Analyzing Complex Information, Second Edition updates and expands on the first edition, bringing a set of techniques and algorithms that are tailored to Big Data projects. The book stresses the point that most data analyses conducted on large, complex data sets can be achieved without the use of specialized suites of software (e.g., Hadoop), and without expensive hardware (e.g., supercomputers). The core of every algorithm described in the book can be implemented in a few lines of code using just about any popular programming language (Python snippets are provided).Through the use of new multiple examples, this edition demonstrates that if we understand our data, and if we know how to ask the right questions, we can learn a great deal from large and complex data collections. The book will assist students and professionals from all scientific backgrounds who are interested in stepping outside the traditional boundaries of their chosen academic disciplines.- Presents new methodologies that are widely applicable to just about any project involving large and complex datasets- Offers readers informative new case studies across a range scientific and engineering disciplines- Provides insights into semantics, identification, de-identification, vulnerabilities and regulatory/legal issues- Utilizes a combination of pseudocode and very short snippets of Python code to show readers how they may develop their own projects without downloading or learning new software

Evolution's Clinical Guidebook: Translating Ancient Genes into Precision Medicine demonstrates, through well-documented examples, how an understanding of the phylogenetic ancestry of humans allows us to make sense out of the flood of genetic data streaming from modern laboratories and how it can lead us to new ways to prevent, diagnose and treat diseases. Topics cover evolution and human genome, meiosis and other recombinants events, embryology, speciation, phylogeny, rare and common diseases, and the evolution of aging. This book is a valuable source for bioinformaticians and those in the biomedical field who need knowledge, down to gene level, to fully comprehend currently available data.

Offers an innovative approach, focusing on how disease-associated pathways evolved Explains how the fields of phylogeny and embryology have become closely tied to the fields of genetics and bioinformatics Demonstrates how students and biomedical professionals can apply the knowledge obtained in this book to the theory and practice of precision medicine

Evolution''s Clinical Guidebook: Translating Ancient Genes into Precision Medicine demonstrates, through well-documented examples, how an understanding of the phylogenetic ancestry of humans allows us to make sense out of the flood of genetic data streaming from modern laboratories and how it can lead us to new ways to prevent, diagnose and treat diseases. Topics cover evolution and human genome, meiosis and other recombinants events, embryology, speciation, phylogeny, rare and common diseases, and the evolution of aging. This book is a valuable source for bioinformaticians and those in the biomedical field who need knowledge, down to gene level, to fully comprehend currently available data.- Offers an innovative approach, focusing on how disease-associated pathways evolved- Explains how the fields of phylogeny and embryology have become closely tied to the fields of genetics and bioinformatics- Demonstrates how students and biomedical professionals can apply the knowledge obtained in this book to the theory and practice of precision medicine

Taxonomic Guide to Infectious Diseases: Understanding the Biologic Classes of Pathogenic Organisms, Second Edition tackles the complexity of clinical microbiology by assigning every infectious organism to one of 40+ taxonomic classes and providing a description of the defining traits that apply to all the organisms within each class. This edition is an updated, revised and greatly expanded guide to the classes of organisms that infect humans. This book will provide students and clinicians alike with a simplified way to understand the complex fields of clinical microbiology and parasitology.

Focuses on human disease processes and includes numerous clinical tips for healthcare providers Describes the principles of classification and explains why the science of taxonomy is vital to the fields of bioinformatics and modern disease research Provides images of prototypical organisms for taxonomic classes Includes a section that lists common taxonomic pitfalls and how they can be avoided

Taxonomic Guide to Infectious Diseases: Understanding the Biologic Classes of Pathogenic Organisms, Second Edition tackles the complexity of clinical microbiology by assigning every infectious organism to one of 40+ taxonomic classes and providing a description of the defining traits that apply to all the organisms within each class. This edition is an updated, revised and greatly expanded guide to the classes of organisms that infect humans. This book will provide students and clinicians alike with a simplified way to understand the complex fields of clinical microbiology and parasitology.- Focuses on human disease processes and includes numerous clinical tips for healthcare providers- Describes the principles of classification and explains why the science of taxonomy is vital to the fields of bioinformatics and modern disease research- Provides images of prototypical organisms for taxonomic classes- Includes a section that lists common taxonomic pitfalls and how they can be avoided

All too often, individuals engaged in the biomedical sciences assume that numeric data must be left to the proper authorities (e.g., statisticians and data analysts) who are trained to apply sophisticated mathematical algorithms to sets of data. This is a terrible mistake. Individuals with keen observational skills, regardless of their mathematical training, are in the best position to draw correct inferences from their own data and to guide the subsequent implementation of robust, mathematical analyses. Volume 2 of Logic and Critical Thinking in the Biomedical Sciences provides readers with a repertoire of deductive non-mathematical methods that will help them draw useful inferences from their own data.Volumes 1 and 2 of Logic and Critical Thinking in the Biomedical Sciences are written for biomedical scientists and college-level students engaged in any of the life sciences, including bioinformatics and related data sciences.- Demonstrates that a great deal can be deduced from quantitative data, without applying any statistical or mathematical analyses- Provides readers with simple techniques for quickly reviewing and finding important relationships hidden within large and complex sets of data- Using examples drawn from the biomedical literature, discusses common pitfalls in data interpretation and how they can be avoided

Science is not a collection of facts. Science is the process by which we draw inferences from facts. Volume I of Logic and Critical Thinking in the Biomedical Sciences invites readers to linger over a collection of common observations to see what inferences can be drawn, when one applies a bit of deductive logic. If we just think about what we observe, it is often possible to discover profound biomedical insights.Volumes 1 and 2 of Logic and Critical Thinking in the Biomedical Sciences are written for biomedical scientists and college-level students engaged in any of the life sciences, including bioinformatics and related data sciences. Provides a strong introduction to deductive methods that can be directly applied to the biomedical sciences Using hundreds of examples, shows how creative scientists draw important inferences from observations that are often ignored by their peers Discusses complex biological and medical concepts in a relaxed manner, intended to focus the reader's attention on the deductive process, without dwelling excessively on details

All too often, individuals engaged in the biomedical sciences assume that numeric data must be left to the proper authorities (e.g., statisticians and data analysts) who are trained to apply sophisticated mathematical algorithms to sets of data. This is a terrible mistake. Individuals with keen observational skills, regardless of their mathematical training, are in the best position to draw correct inferences from their own data and to guide the subsequent implementation of robust, mathematical analyses. Volume 2 of Logic and Critical Thinking in the Biomedical Sciences provides readers with a repertoire of deductive non-mathematical methods that will help them draw useful inferences from their own data.Volumes 1 and 2 of Logic and Critical Thinking in the Biomedical Sciences are written for biomedical scientists and college-level students engaged in any of the life sciences, including bioinformatics and related data sciences. Demonstrates that a great deal can be deduced from quantitative data, without applying any statistical or mathematical analyses Provides readers with simple techniques for quickly reviewing and finding important relationships hidden within large and complex sets of data Using examples drawn from the biomedical literature, discusses common pitfalls in data interpretation and how they can be avoided

Science is not a collection of facts. Science is the process by which we draw inferences from facts. Volume I of Logic and Critical Thinking in the Biomedical Sciences invites readers to linger over a collection of common observations to see what inferences can be drawn, when one applies a bit of deductive logic. If we just think about what we observe, it is often possible to discover profound biomedical insights.Volumes 1 and 2 of Logic and Critical Thinking in the Biomedical Sciences are written for biomedical scientists and college-level students engaged in any of the life sciences, including bioinformatics and related data sciences.- Provides a strong introduction to deductive methods that can be directly applied to the biomedical sciences- Using hundreds of examples, shows how creative scientists draw important inferences from observations that are often ignored by their peers- Discusses complex biological and medical concepts in a relaxed manner, intended to focus the reader''s attention on the deductive process, without dwelling excessively on details

Classification Made Relevant: How Scientists Build and Use Classifications and Ontologies explains how classifications and ontologies are designed and used to analyze scientific information. The book presents the fundamentals of classification, leading up to a description of how computer scientists use object-oriented programming languages to model classifications and ontologies. Numerous examples are chosen from the Classification of Life, the Periodic Table of the Elements, and the symmetry relationships contained within the Classification Theorem of Finite Simple Groups. When these three classifications are tied together, they provide a relational hierarchy connecting all of the natural sciences.

The book's chapters introduce and describe general concepts that can be understood by any intelligent reader. With each new concept, they follow practical examples selected from various scientific disciplines. In these cases, technical points and specialized vocabulary are linked to glossary items where the item is clarified and expanded.

Explains the theory and practice of classification, emphasizing the importance of classifications and ontologies to the modern fields of mathematics, physics, chemistry, biology and medicine Includes numerous real-world examples that demonstrate how bad construction technique can destroy the value of classifications and ontologies Explains how we define and understand the relationships among the classes within a classification and how the properties of a class are inherited by its subclasses Describes ontologies and how they differ from classifications and explains conditions under which ontologies are useful

Classification Made Relevant: How Scientists Build and Use Classifications and Ontologies explains how classifications and ontologies are designed and used to analyze scientific information. The book presents the fundamentals of classification, leading up to a description of how computer scientists use object-oriented programming languages to model classifications and ontologies. Numerous examples are chosen from the Classification of Life, the Periodic Table of the Elements, and the symmetry relationships contained within the Classification Theorem of Finite Simple Groups. When these three classifications are tied together, they provide a relational hierarchy connecting all of the natural sciences.The book''s chapters introduce and describe general concepts that can be understood by any intelligent reader. With each new concept, they follow practical examples selected from various scientific disciplines. In these cases, technical points and specialized vocabulary are linked to glossary items where the item is clarified and expanded.- Explains the theory and practice of classification, emphasizing the importance of classifications and ontologies to the modern fields of mathematics, physics, chemistry, biology and medicine- Includes numerous real-world examples that demonstrate how bad construction technique can destroy the value of classifications and ontologies- Explains how we define and understand the relationships among the classes within a classification and how the properties of a class are inherited by its subclasses- Describes ontologies and how they differ from classifications and explains conditions under which ontologies are useful

Ideal for healthcare workers, students and biomedical researchers who wish to use informatics technologies in their own clinics and laboratories, Biomedical Informatics describes the fundamental issues and questions in the field and reviews the different types of biomedical data resources and open source tools needed to fully utilize biomedical data. You are shown how to navigate through the legal, ethical, and technical hazards of biomedical informatics to become self-sufficient and productive. You will finish with an understanding of how to acquire, organize, annotate, and share biomedical data, how to render confidential data harmless through de-identification, and how to use a variety of free and open source utilities to solve common computational tasks. Berman also discusses how the Perl Language is used in biomedical informatics and provides short Perl scripts that can be applied in the biological research and healthcare settings.

For scientists, students, and curious laypersons, this compilation, Proofs and Logical Arguments Supporting the Foundational Laws of Physics: A Handy Guide for Students and Scientists examines the most important laws and relationships taught in science courses, attaching a short and accessible proof or logical argument for each assertion.Every thoughtful person should seek to understand why we think we know what we say we know about the natural world. Otherwise, we may as well surrender ourselves to a world ruled by magic. In 136 essays, readers are provided with proofs and logical arguments supporting the laws and relationships that serve as the foundation of our rational understanding of reality. Among the essays included in this book, we will find proofs of Pauli’s exclusion principle, Heisenberg’s uncertainty principle, the principles of special relativity, the Schrodinger wave equation, Noether’s theorem, and many of the laws of physics and chemistry that no scientist should accept on blind faith alone.Laypersons will find that the ideas discussed in this volume are always thought-provoking and sometimes inspiring. For university undergraduates, the book will serve as an introduction to the core sciences. Graduate students may find this book to be a handy cross-disciplinary reference that explains how the tools of their own selected discipline have emerged from fundamental principles that unify all the sciences.Jules J. Berman received two baccalaureate degrees from MIT (from the Department of Mathematics, and from the Department of Earth and Planetary Sciences). He holds a PhD from Temple University, and an MD, from the University of Miami. His postdoctoral studies were completed at the US National Institutes of Health, and his residency was completed at the George Washington University Medical Center in Washington, DC. Dr. Berman served as Chief of Anatomic Pathology, Surgical Pathology, and Cytopathology at the Veterans administration Medical Center in Baltimore, Maryland, where he also held joint appointments at the University of Maryland Medical Center and at the Johns Hopkins Medical Institutions. In 1998, he transferred back to the US National Institutes of Health, as a Medical Officer, and as the Program Director for Pathology Informatics in the Cancer Diagnosis Program at the National Cancer Institute. Dr. Berman is a past president of the Association for Pathology Informatics, and is the 2011 recipient of the Association’s Lifetime Achievement Award. He has first-authored more than 100 journal articles and has written more than 20 single-author science books.

For scientists, students, and curious laypersons, this compilation, Proofs and Logical Arguments Supporting the Foundational Laws of Physics: A Handy Guide for Students and Scientists examines the most important laws and relationships taught in science courses, attaching a short and accessible proof or logical argument for each assertion.Every thoughtful person should seek to understand why we think we know what we say we know about the natural world. Otherwise, we may as well surrender ourselves to a world ruled by magic. In 136 essays, readers are provided with proofs and logical arguments supporting the laws and relationships that serve as the foundation of our rational understanding of reality. Among the essays included in this book, we will find proofs of Pauli’s exclusion principle, Heisenberg’s uncertainty principle, the principles of special relativity, the Schrodinger wave equation, Noether’s theorem, and many of the laws of physics and chemistry that no scientist should accept on blind faith alone.Laypersons will find that the ideas discussed in this volume are always thought-provoking and sometimes inspiring. For university undergraduates, the book will serve as an introduction to the core sciences. Graduate students may find this book to be a handy cross-disciplinary reference that explains how the tools of their own selected discipline have emerged from fundamental principles that unify all the sciences.Jules J. Berman received two baccalaureate degrees from MIT (from the Department of Mathematics, and from the Department of Earth and Planetary Sciences). He holds a PhD from Temple University, and an MD, from the University of Miami. His postdoctoral studies were completed at the US National Institutes of Health, and his residency was completed at the George Washington University Medical Center in Washington, DC. Dr. Berman served as Chief of Anatomic Pathology, Surgical Pathology, and Cytopathology at the Veterans administration Medical Center in Baltimore, Maryland, where he also held joint appointments at the University of Maryland Medical Center and at the Johns Hopkins Medical Institutions. In 1998, he transferred back to the US National Institutes of Health, as a Medical Officer, and as the Program Director for Pathology Informatics in the Cancer Diagnosis Program at the National Cancer Institute. Dr. Berman is a past president of the Association for Pathology Informatics, and is the 2011 recipient of the Association’s Lifetime Achievement Award. He has first-authored more than 100 journal articles and has written more than 20 single-author science books.

Too often, healthcare workers are led to believe that medical informatics is a complex field that can only be mastered by teams of professional programmers. This is simply not the case. With just a few dozen simple algorithms, easily implemented with open source programming languages, you can fully utilize the medical information contained in clini