Biology is designed for multi-semester biology courses for science majors. It is grounded on an evolutionary basis and includes exciting features that highlight careers in the biological sciences and everyday applications of the concepts at hand. To meet the needs of today’s instructors and students, some content has been strategically condensed while maintaining the overall scope and coverage of traditional texts for this course. Instructors can customize the book, adapting it to the approach that works best in their classroom. Biology also includes an innovative art program that incorporates critical thinking and clicker questions to help students understand—and apply—key concepts.

By the end of this section, you will be able to:Describe epithelial tissuesDiscuss the different types of connective tissues in animalsDescribe three types of muscle tissuesDescribe nervous tissue

This resource is a video abstract of a research paper created by Research Square on behalf of its authors. It provides a synopsis that's easy to understand, and can be used to introduce the topics it covers to students, researchers, and the general public. The video's transcript is also provided in full, with a portion provided below for preview:

"Cell-free DNA (cfDNA) sequencing has been helpful in diagnosing infectious disease. It allows a pathologist to unbiasly search for all pathogens in a sample instead of culturing for specific ones. However, the presence of contaminant DNA and misidentification of microbes are possible, leading to false-positive diagnoses when using this highly sensitive diagnostic method. A recent study describes a new bioinformatics platform called Low Biomass Background Correction, or LBBC, which searches for and helps remove contaminant DNA from cfDNA samples. In a urinary tract infection screen, LBBC reduced the false positive rate while minimally affecting the true positive rate. And among pregnant women, it allowed researchers to generate a new cfDNA dataset for amniotic fluids. That could help clinicians identify intra-amniotic infection more easily while supporting the view that amniotic fluid is sterile during normal pregnancy..."

The rest of the transcript, along with a link to the research itself, is available on the resource itself.

This resource is a video abstract of a research paper created by Research Square on behalf of its authors. It provides a synopsis that's easy to understand, and can be used to introduce the topics it covers to students, researchers, and the general public. The video's transcript is also provided in full, with a portion provided below for preview:

"Artificial intelligence is making rapid advances in medicine. Already, there are machine learning algorithms that can outperform doctors in some medical fields. There’s only one fairly big problem: experts aren’t quite sure how these algorithms work. While designers know full well what goes into the A-I systems they build and what comes out, the learning part in between is often too complex to comprehend. To their users, machine learning algorithms are effectively black boxes. Now, researchers from the RIKEN Center for Advanced Intelligence Project in Japan are lifting the lid. They’ve developed a deep-learning system that can outperform human experts in predicting whether prostate cancer will reoccur within one year. More importantly, the deep learning system they developed can acquire human-understandable features from unannotated pathology images to offer up critical clues that could help humans make better diagnoses themselves..."

The rest of the transcript, along with a link to the research itself, is available on the resource itself.