WORKING WITH PATIENTS WITH SUBSTANCE USE DISORDERS OR HIGH RISK This optional online course opportunity is made possible through a joint partnership with University of Virginia School of Medicine (UVASOM) and Nursing (SON) and the American Academy of Addiction Psychiatry (AAAP), DATA 2000 sponsor. This content was created by the AAAP and has been used with permission. The course consists of one 1-hour session for healthcare providers who wish to enhance their effectiveness in treating high-risk patients or those with Substance Use Disorder. Motivational Interviewing has been quite effective in not only treating these clients but many other clients with other chronic illnesses as well. This webinar will discuss Motivational Enhancement Techniques to assist clinicians in treating patients with opioid substance use disorders. The session includes a pre and post self-assessment and one recorded audio session. After completing all components, learners will be directed to complete an evaluation to receive credit. Estimated time to complete this activity: 1.0 hours Release date: March 1, 2020 UVA Grant Dates: 09/30/2019 – 09/29/2022 Funding for this initiative was made possible by a grant from SAMHSA. The views expressed in written conference materials or publications and by speakers and moderators do not necessarily reflect the official policies of the Department of Health and Human Services; nor does mention of trade names, commercial practices, or organizations imply endorsement by the U.S. Government.

How do we make decisions as consumers? What do we pay attention to, and how do our initial responses predict our final choices? To what extent are these processes unconscious and cannot be reflected in overt reports? This course will provide you with an introduction to some of the most basic methods in the emerging fields of consumer neuroscience and neuromarketing. You will learn about the methods employed and what they mean. You will learn about the basic brain mechanisms in consumer choice, and how to stay updated on these topics. The course will give an overview of the current and future uses of neuroscience in business.

What we eat and how we produce that food have significant effects on human health and the sustainability of our planet. But what is a ‘sustainable’ diet? A sustainable diet, as defined by the FAO, promotes health and well-being and provides food security for the present population while sustaining human and natural resources for future generations. This short course looks at the urgent need to address the sustainability of our food systems, including better understanding the complex relationship between diet and climate change. We’ll explore current research on dietary shifts needed in high, middle, and low-income countries to achieve both sustainability and food security goals and discuss evidence-based strategies to promote sustainable diets. This course is offered by the Johns Hopkins Center for a Livable Future and draws from our graduate-level food systems curriculum at the Bloomberg School of Public Health. You may also be interested in our eight-week flagship Coursera course, “An Introduction to the US Food System: Perspectives from Public Health”.

The past 15 years have been exciting ones in plant biology. Hundreds of plant genomes have been sequenced, RNA-seq has enabled transcriptome-wide expression profiling, and a proliferation of "-seq"-based methods has permitted protein-protein and protein-DNA interactions to be determined cheaply and in a high-throughput manner. These data sets in turn allow us to generate hypotheses at the click of a mouse or tap of a finger. In Plant Bioinformatics on Coursera.org, we covered 33 plant-specific online tools from genome browsers to transcriptomic data mining to promoter/network analyses and others, and in this Plant Bioinformatics Capstone we'll use these tools to hypothesize a biological role for a gene of unknown function, summarized in a written lab report. This course is part of a Plant Bioinformatics Specialization on Coursera, which introduces core bioinformatic competencies and resources, such as NCBI's Genbank, Blast, multiple sequence alignments, phylogenetics in Bioinformatic Methods I, followed by protein-protein interactions, structural bioinformatics and RNA-seq analysis in Bioinformatic Methods II, in addition to the plant-specific concepts and tools introduced in Plant Bioinformatics and the Plant Bioinformatics Capstone. This course/capstone was developed with funding from the University of Toronto's Faculty of Arts and Science Open Course Initiative Fund (OCIF) and was implemented by Eddi Esteban, Will Heikoop and Nicholas Provart. Asher Pasha programmed a gene ID randomizer.

This course will focus on developing integrative skills through directed reading and analysis of the current primary literature to enable the student to develop the capstone project as the overall final exam for the specialization in systems biology.

In this course, we dive into the topic of child development. You will learn that child development is complex and is influenced by a surprisingly rich number of factors at many different levels of organization. You will see that development can be studied in various domains and at various levels of understanding, from different angles and disciplines. Importantly, you will come to understand that processes within and between the various domains and levels of organization continuously interact to shape development. This implies that understanding development requires taking an interdisciplinary approach. In order to help you organize your thinking about this complexity you will learn more about the developmental systems approach and theories related to that. We will look, amongst other things, at the brain and motor development, cognitive, language and social-emotional development and all the factors that have an influence on development. To illustrate how an interdisciplinary approach helps to understand child development you will see how researchers from various disciplines and backgrounds study child development at Utrecht University. These scientists will give a look in their field of knowledge and their collaboration with colleagues, to illustrate how the theory you will learn translates to practice. In sum, we invite you to follow us on a journey to understand child development from synapse to society!

Career prospects are bright for those qualified to work in healthcare data analytics. Perhaps you work in data analytics, but are considering a move into healthcare where your work can improve people’s quality of life. If so, this course gives you a glimpse into why this work matters, what you’d be doing in this role, and what takes place on the Path to Value where data is gathered from patients at the point of care, moves into data warehouses to be prepared for analysis, then moves along the data pipeline to be transformed into valuable insights that can save lives, reduce costs, to improve healthcare and make it more accessible and affordable. Perhaps you work in healthcare but are considering a transition into a new role. If so, this course will help you see if this career path is one you want to pursue. You’ll get an overview of common data models and their uses. You’ll learn how various systems integrate data, how to ensure clear communication, measure and improve data quality. Data analytics in healthcare serves doctors, clinicians, patients, care providers, and those who carry out the business of improving health outcomes. This course of study will give you a clear picture of data analysis in today’s fast-changing healthcare field and the opportunities it holds for you.

Introduces to the commands that you need to manage and analyze directories, files, and large sets of genomic data. This is the fourth course in the Genomic Big Data Science Specialization from Johns Hopkins University.

Course Description What is our role in the universe as human agents capable of knowledge? What makes us intelligent cognitive agents seemingly endowed with consciousness? This is the second part of the course 'Philosophy and the Sciences', dedicated to Philosophy of the Cognitive Sciences. Scientific research across the cognitive sciences has raised pressing questions for philosophers. The goal of this course is to introduce you to some of the main areas and topics at the key juncture between philosophy and the cognitive sciences. Each week we will introduce you to some of these important questions at the forefront of scientific research. We will explain the science behind each topic in a simple, non-technical way, while also addressing the philosophical and conceptual questions arising from it. Areas you’ll learn about will include: Philosophy of psychology, among whose issues we will cover the evolution of the human mind and the nature of consciousness. Philosophy of neurosciences, where we’ll consider the nature of human cognition and the relation between mind, machines, and the environment. Learning objectives Gain a fairly well-rounded view on selected areas and topics at the intersection of philosophy and the sciences Understand some key questions, and conceptual problems arising in the cognitive sciences. Develop critical skills to evaluate and assess these problems. Suggested Readings To accompany 'Philosophy and the Sciences', we are pleased to announce a tie-in book from Routledge entitled 'Philosophy and the Sciences for Everyone'. This course companion to the 'Philosophy and the Sciences' course was written by the Edinburgh Philosophy and the Sciences team expressly with the needs of MOOC students in mind. 'Philosophy and the Sciences for Everyone' contains clear and user-friendly chapters, chapter summaries, glossary, study questions, suggestions for further reading and guides to online resources. Please note, this companion book is optional - all the resources needed to complete the course are available freely and listed on the course site.

In this course, we’re going to go over analytical solutions to common healthcare problems. I will review these business problems and you’ll build out various data structures to organize your data. We’ll then explore ways to group data and categorize medical codes into analytical categories. You will then be able to extract, transform, and load data into data structures required for solving medical problems and be able to also harmonize data from multiple sources. Finally, you will create a data dictionary to communicate the source and value of data. Creating these artifacts of data processes is a key skill when working with healthcare data.