Howard Hughes Medical Institute

Image quality assessment: from error visibility to structural similarity

Objective methods for assessing perceptual image quality traditionally attempted to quantify the visibility of errors (differences) between a distorted image and a reference image using a variety of known properties of the human visual system. Under the assumption that human visual perception is highly adapted for extracting structural information from a scene, we introduce an alternative complementary framework for quality assessment based on the degradation of structural information. As a specific example of this concept, we develop a Structural Similarity Index and demonstrate its promise through a set of intuitive examples, as well as comparison to both subjective ratings and state-of-the-art objective methods on a database of images compressed with JPEG and JPEG2000.

The new frontier of genome engineering with CRISPR-Cas9

The advent of facile genome engineering using the bacterial RNA-guided CRISPR-Cas9 system in animals and plants is transforming biology. We review the history of CRISPR (clustered regularly interspaced palindromic repeat) biology from its initial discovery through the elucidation of the CRISPR-Cas9 enzyme mechanism, which has set the stage for remarkable developments using this technology to modify, regulate, or mark genomic loci in a wide variety of cells and organisms from all three domains of life. These results highlight a new era in which genomic manipulation is no longer a bottleneck to experiments, paving the way toward fundamental discoveries in biology, with applications in all branches of biotechnology, as well as strategies for human therapeutics.

The Molecular Biology of Memory Storage: A Dialogue Between Genes and Synapses

One of the most remarkable aspects of an animal's behavior is the ability to modify that behavior by learning, an ability that reaches its highest form in human beings. For me, learning and memory have proven to be endlessly fascinating mental processes because they address one of the fundamental features of human activity: our ability to acquire new ideas from experience and to retain these ideas over time in memory. Moreover, unlike other mental processes such as thought, language, and consciousness, learning seemed from the outset to be readily accessible to cellular and molecular analysis. I, therefore, have been curious to know: What changes in the brain when we learn? And, once something is learned, how is that information retained in the brain? I have tried to address these questions through a reductionist approach that would allow me to investigate elementary forms of learning and memory at a cellular molecular level-as specific molecular activities within identified nerve cells.

The Molecular Biology of Axon Guidance

Neuronal growth cones navigate over long distances along specific pathways to find their correct targets. The mechanisms and molecules that direct this pathfinding are the topics of this review. Growth cones appear to be guided by at least four different mechanisms: contact attraction, chemoattraction, contact repulsion, and chemorepulsion. Evidence is accumulating that these mechanisms act simultaneously and in a coordinated manner to direct pathfinding and that they are mediated by mechanistically and evolutionarily conserved ligand-receptor systems.

Hematopoiesis: An Evolving Paradigm for Stem Cell Biology

The Biology of Chromatin Remodeling Complexes

The packaging of chromosomal DNA by nucleosomes condenses and organizes the genome, but occludes many regulatory DNA elements. However, this constraint also allows nucleosomes and other chromatin components to actively participate in the regulation of transcription, chromosome segregation, DNA replication, and DNA repair. To enable dynamic access to packaged DNA and to tailor nucleosome composition in chromosomal regions, cells have evolved a set of specialized chromatin remodeling complexes (remodelers). Remodelers use the energy of ATP hydrolysis to move, destabilize, eject, or restructure nucleosomes. Here, we address many aspects of remodeler biology: their targeting, mechanism, regulation, shared and unique properties, and specialization for particular biological processes. We also address roles for remodelers in development, cancer, and human syndromes.

Opportunities and obstacles for deep learning in biology and medicine

Deep learning describes a class of machine learning algorithms that are capable of combining raw inputs into layers of intermediate features. These algorithms have recently shown impressive results across a variety of domains. Biology and medicine are data-rich disciplines, but the data are complex and often ill-understood. Hence, deep learning techniques may be particularly well suited to solve problems of these fields. We examine applications of deep learning to a variety of biomedical problems-patient classification, fundamental biological processes and treatment of patients-and discuss whether deep learning will be able to transform these tasks or if the biomedical sphere poses unique challenges. Following from an extensive literature review, we find that deep learning has yet to revolutionize biomedicine or definitively resolve any of the most pressing challenges in the field, but promising advances have been made on the prior state of the art. Even though improvements over previous baselines have been modest in general, the recent progress indicates that deep learning methods will provide valuable means for speeding up or aiding human investigation. Though progress has been made linking a specific neural network's prediction to input features, understanding how users should interpret these models to make testable hypotheses about the system under study remains an open challenge. Furthermore, the limited amount of labelled data for training presents problems in some domains, as do legal and privacy constraints on work with sensitive health records. Nonetheless, we foresee deep learning enabling changes at both bench and bedside with the potential to transform several areas of biology and medicine.

The Biology of NKT Cells

Recognized more than a decade ago, NKT cells differentiate from mainstream thymic precursors through instructive signals emanating during TCR engagement by CD1d-expressing cortical thymocytes. Their semi-invariant alphabeta TCRs recognize isoglobotrihexosylceramide, a mammalian glycosphingolipid, as well as microbial alpha-glycuronylceramides found in the cell wall of Gram-negative, lipopolysaccharide-negative bacteria. This dual recognition of self and microbial ligands underlies innate-like antimicrobial functions mediated by CD40L induction and massive Th1 and Th2 cytokine and chemokine release. Through reciprocal activation of NKT cells and dendritic cells, synthetic NKT ligands constitute promising new vaccine adjuvants. NKT cells also regulate a range of immunopathological conditions, but the mechanisms and the ligands involved remain unknown. NKT cell biology has emerged as a new field of research at the frontier between innate and adaptive immunity, providing a powerful model to study fundamental aspects of the cell and structural biology of glycolipid trafficking, processing, and recognition.

Fat and Beyond: The Diverse Biology of PPARγ

The nuclear receptor PPARgamma is a ligand-activated transcription factor that plays an important role in the control of gene expression linked to a variety of physiological processes. PPARgamma was initially characterized as the master regulator for the development of adipose cells. Ligands for PPARgamma include naturally occurring fatty acids and the thiazolidinedione (TZD) class of antidiabetic drugs. Activation of PPARgamma improves insulin sensitivity in rodents and humans through a combination of metabolic actions, including partitioning of lipid stores and the regulation of metabolic and inflammatory mediators termed adipokines. PPARgamma signaling has also been implicated in the control of cell proliferation, atherosclerosis, macrophage function, and immunity. Here, we review recent advances in our understanding of the diverse biological actions of PPARgamma with an eye toward the expanding therapeutic potential of PPARgamma agonist drugs.

The Emergence of lncRNAs in Cancer Biology

Abstract The discovery of numerous noncoding RNA (ncRNA) transcripts in species from yeast to mammals has dramatically altered our understanding of cell biology, especially the biology of diseases such as cancer. In humans, the identification of abundant long ncRNA (lncRNA) >200 bp has catalyzed their characterization as critical components of cancer biology. Recently, roles for lncRNAs as drivers of tumor suppressive and oncogenic functions have appeared in prevalent cancer types, such as breast and prostate cancer. In this review, we highlight the emerging impact of ncRNAs in cancer research, with a particular focus on the mechanisms and functions of lncRNAs. Significance: lncRNAs represent the leading edge of cancer research. Their identity, function, and dysregulation in cancer are only beginning to be understood, and recent data suggest that they may serve as master drivers of carcinogenesis. Increased research on these RNAs will lead to a greater understanding of cancer cell function and may lead to novel clinical applications in oncology. Cancer Discovery; 1(5): 391–407. ©2011 AACR.