The greater challenge of this book has been sorting through a conspiracy of biased data interpretation more than verification of the actual data. It is as though the institutional foundation of academia has rigidly defined parameters of historically acceptable facts, ignoring any evidence that does not fit within the target of measured agreement. Not only are these several conclusions contrary to my personal experience, but they border on absurdity in the open arena of logical discourse. Therefore, at the risk of sounding foolish to the world of secular thinking and sounding on the fringe to the minds of those most religious, I have endeavored to remain true to my conscience through this intricate process of human analysis. Even though I have explored the reasonable expectations constrained through many subjects of intellectual controversy, I am kept in remembrance there is no greater meaning than love. For by love the worlds are framed.

“One of our great visionaries.” —Dr. Wayne W. Dyer “A rare blend of scientist, visionary, and scholar.” —Deepak Chopra A scholar and New York Times–bestselling author shares his shocking theory of an ancient language—found in the decoded elements of our DNA—that shines new light on the mysteries of existence. What would it mean to discover an ancient language—a literal message—hidden within the DNA of life itself? What we once believed of our past is about to change. A coded message has been found within the molecules of life, deep within the DNA in each cell of our bodies. Through a remarkable discovery linking Biblical alphabets to our genetic code, the “language of life” may now be read as the ancient letters of a timeless message. Regardless of race, religion, heritage, or lifestyle, the message is the same in each cell of every woman, child, and man, past and present. Sharing all-new, fascinating research, Gregg Braden discusses the life-changing discovery that led him from a successful career in the aerospace and defense industries to an extensive 12-year study of the most sacred and honored traditions of humankind.

A Best Book of 2021 by Bloomberg BusinessWeek, Time, and The Washington Post The bestselling author of Leonardo da Vinci and Steve Jobs returns with a “compelling” (The Washington Post) account of how Nobel Prize winner Jennifer Doudna and her colleagues launched a revolution that will allow us to cure diseases, fend off viruses, and have healthier babies. When Jennifer Doudna was in sixth grade, she came home one day to find that her dad had left a paperback titled The Double Helix on her bed. She put it aside, thinking it was one of those detective tales she loved. When she read it on a rainy Saturday, she discovered she was right, in a way. As she sped through the pages, she became enthralled by the intense drama behind the competition to discover the code of life. Even though her high school counselor told her girls didn’t become scientists, she decided she would. Driven by a passion to understand how nature works and to turn discoveries into inventions, she would help to make what the book’s author, James Watson, told her was the most important biological advance since his codiscovery of the structure of DNA. She and her collaborators turned a curiosity of nature into an invention that will transform the human race: an easy-to-use tool that can edit DNA. Known as CRISPR, it opened a brave new world of medical miracles and moral questions. The development of CRISPR and the race to create vaccines for coronavirus will hasten our transition to the next great innovation revolution. The past half-century has been a digital age, based on the microchip, computer, and internet. Now we are entering a life-science revolution. Children who study digital coding will be joined by those who study genetic code. Should we use our new evolution-hacking powers to make us less susceptible to viruses? What a wonderful boon that would be! And what about preventing depression? Hmmm…Should we allow parents, if they can afford it, to enhance the height or muscles or IQ of their kids? After helping to discover CRISPR, Doudna became a leader in wrestling with these moral issues and, with her collaborator Emmanuelle Charpentier, won the Nobel Prize in 2020. Her story is an “enthralling detective story” (Oprah Daily) that involves the most profound wonders of nature, from the origins of life to the future of our species.

This Festschrift volume contains the proceedings of the conference Mathematical Methods in Computer Science, MMICS 2008, held December 2008, in Karlsruhe, Germany, in memory of Thomas Beth. The themes of the conference reflect his many interests.

Discovering Computer Science: Interdisciplinary Problems, Principles, and Python Programming introduces computational problem solving as a vehicle of discovery in a wide variety of disciplines. With a principles-oriented introduction to computational thinking, the text provides a broader and deeper introduction to computer science than typical introductory programming books. Organized around interdisciplinary problem domains, rather than programming language features, each chapter guides students through increasingly sophisticated algorithmic and programming techniques. The author uses a spiral approach to introduce Python language features in increasingly complex contexts as the book progresses. The text places programming in the context of fundamental computer science principles, such as abstraction, efficiency, and algorithmic techniques, and offers overviews of fundamental topics that are traditionally put off until later courses. The book includes thirty well-developed independent projects that encourage students to explore questions across disciplinary boundaries. Each is motivated by a problem that students can investigate by developing algorithms and implementing them as Python programs. The book's accompanying website — http://discoverCS.denison.edu — includes sample code and data files, pointers for further exploration, errata, and links to Python language references. Containing over 600 homework exercises and over 300 integrated reflection questions, this textbook is appropriate for a first computer science course for computer science majors, an introductory scientific computing course or, at a slower pace, any introductory computer science course.

In the captivating journey of our data science workshop, we embarked on the exploration of Chronic Kidney Disease classification and prediction. Our quest began with a thorough dive into data exploration, where we meticulously delved into the dataset's intricacies to unearth hidden patterns and insights. We analyzed the distribution of categorized features, unraveling the nuances that underlie chronic kidney disease. Guided by the principles of machine learning, we embarked on the quest to build predictive models. With the aid of grid search, we fine-tuned our machine learning algorithms, optimizing their hyperparameters for peak performance. Each model, whether K-Nearest Neighbors, Decision Trees, Random Forests, Gradient Boosting, Naive Bayes, Extreme Gradient Boosting, Light Gradient Boosting, or Multi-Layer Perceptron, was meticulously trained and tested, paving the way for robust predictions. The voyage into the realm of deep learning took us further, as we harnessed the power of Artificial Neural Networks (ANNs). By constructing intricate architectures, we designed ANNs to discern intricate patterns from the data. Leveraging the prowess of TensorFlow, we artfully crafted layers, each contributing to the ANN's comprehension of the underlying dynamics. This marked our initial foray into the world of deep learning. Our expedition, however, did not conclude with ANNs. We ventured deeper into the abyss of deep learning, uncovering the potential of Long Short-Term Memory (LSTM) networks. These networks, attuned to sequential data, unraveled temporal dependencies within the dataset, fortifying our predictive capabilities. Diving even further, we encountered Self-Organizing Maps (SOMs) and Restricted Boltzmann Machines (RBMs). These innovative models, rooted in unsupervised learning, unmasked underlying structures in the dataset. As our understanding of the data deepened, so did our repertoire of tools for prediction. Autoencoders, our final frontier in deep learning, emerged as our champions in dimensionality reduction and feature learning. These unsupervised neural networks transformed complex data into compact, meaningful representations, guiding our predictive models with newfound efficiency. To furnish a granular understanding of model behavior, we employed the classification report, which delineated precision, recall, and F1-Score for each class, providing a comprehensive snapshot of the model's predictive capacity across diverse categories. The confusion matrix emerged as a tangible visualization, detailing the interplay between true positives, true negatives, false positives, and false negatives. We also harnessed ROC and precision-recall curves to illuminate the dynamic interplay between true positive rate and false positive rate, vital when tackling imbalanced datasets. For regression tasks, MSE and its counterpart RMSE quantified the average squared differences between predictions and actual values, facilitating an insightful assessment of model fit. Further enhancing our toolkit, the R-squared (R2) score unveiled the extent to which the model explained variance in the dependent variable, offering a valuable gauge of overall performance. Collectively, this ensemble of metrics enabled us to make astute model decisions, optimize hyperparameters, and gauge the models' fitness for accurate disease prognosis in a clinical context. Amidst this whirlwind of data exploration and model construction, our GUI using PyQt emerged as a beacon of user-friendly interaction. Through its intuitive interface, users navigated seamlessly between model selection, training, and prediction. Our GUI encapsulated the intricacies of our journey, bridging the gap between data science and user experience. In the end, our odyssey illuminated the intricate landscape of Chronic Kidney Disease classification and prediction. We harnessed the power of both machine learning and deep learning, uncovering hidden insights and propelling our predictive capabilities to new heights. Our journey transcended the realms of data, algorithms, and interfaces, leaving an indelible mark on the crossroads of science and innovation.