Hasil untuk "Biology"

V. W. Yong, C. Power, P. Forsyth et al.

Matrix metalloproteinases (MMPs) have been implicated in several diseases of the nervous system. Here we review the evidence that supports this idea and discuss the possible mechanisms of MMP action. We then consider some of the beneficial functions of MMPs during neural development and speculate on their roles in repair after brain injury. We also introduce a family of proteins known as ADAMs (a disintegrin and metalloproteinase), as some of the properties previously ascribed to MMPs are possibly the result of ADAM activity.

M. Linder, M. Hazegh-Azam

In this review, our basic and most recent understanding of copper biochemistry and molecular biology for mammals (including humans) is described. Information is provided on the nutritional biochemistry of copper, including food sources, intestinal absorption, transport, tissue distribution, and excretion, along with descriptions of copper binding proteins and other factors involved and their roles in these processes. The metabolism of copper and its importance for the functions of a roster of vital enzymes is detailed. Its potential toxicology is also addressed. Alterations in copper metabolism associated with genetic and nongenetic diseases are summarized, including potential connections to inflammation, cancer, atherosclerosis, and anemia, and the effects of genetic copper deficiency (Menkes syndrome) and copper overload (Wilson disease). Understanding these diseases suggests new ways of viewing the normal functions of copper and provides new insights into the details of copper transport and distribution in mammals.

R. Sokal, N. Oden

James D. Watson, N. Hopkins, Jeffrey W. Roberts et al.

C. Stockwell, A. Hendry, M. Kinnison

J. Mckinnon, J. Staton, M. Bell et al.

P. K. Endress, Brigitta Steiner-Gafner

P. Sunnucks

T. Fehniger, M. Caligiuri

J. Dingle, R. Dean, P. Jacques et al.

J. Nicholson, I. Wilson

A. Keys, J. Brozek, A. Henschel et al.

B. Scholkopf, K. Tsuda, Jean-Philippe Vert

G. Cumming, F. Fidler, D. Vaux

Error bars commonly appear in figures in publications, but experimental biologists are often unsure how they should be used and interpreted. In this article we illustrate some basic features of error bars and explain how they can help communicate data and assist correct interpretation. Error bars may show confidence intervals, standard errors, standard deviations, or other quantities. Different types of error bars give quite different information, and so figure legends must make clear what error bars represent. We suggest eight simple rules to assist with effective use and interpretation of error bars.

B. Boyce, L. Xing

The discovery of the receptor activator of nuclear factor-κB ligand (RANKL)/RANK/osteoprotegerin (OPG) system and its role in the regulation of bone resorption exemplifies how both serendipity and a logic-based approach can identify factors that regulate cell function. Before this discovery in the mid to late 1990s, it had long been recognized that osteoclast formation was regulated by factors expressed by osteoblast/stromal cells, but it had not been anticipated that members of the tumor necrosis factor superfamily of ligands and receptors would be involved or that the factors involved would have extensive functions beyond bone remodeling. RANKL/RANK signaling regulates the formation of multinucleated osteoclasts from their precursors as well as their activation and survival in normal bone remodeling and in a variety of pathologic conditions. OPG protects the skeleton from excessive bone resorption by binding to RANKL and preventing it from binding to its receptor, RANK. Thus, RANKL/OPG ratio is an important determinant of bone mass and skeletal integrity. Genetic studies in mice indicate that RANKL/RANK signaling is also required for lymph node formation and mammary gland lactational hyperplasia, and that OPG also protects arteries from medial calcification. Thus, these tumor necrosis factor superfamily members have important functions outside bone. Although our understanding of the mechanisms whereby they regulate osteoclast formation has advanced rapidly during the past 10 years, many questions remain about their roles in health and disease. Here we review our current understanding of the role of the RANKL/RANK/OPG system in bone and other tissues.

E. Gelmann

H. Roy, S. Bhardwaj, S. Ylä-Herttuala

G. Lemke

C. Gutjahr, M. Parniske

Abhiraam Eranti, Yogesh Tewari, Rafael Palacios et al.

Deep-learning methods have boosted the analytical power of Raman spectroscopy, yet they still require large, task-specific, labeled datasets and often fail to transfer across application domains. The study explores pre-trained encoders as a solution. Pre-trained encoders have significantly impacted Natural Language Processing and Computer Vision with their ability to learn transferable representations that can be applied to a variety of datasets, significantly reducing the amount of time and data required to create capable models. The following work puts forward a new approach that applies these benefits to Raman Spectroscopy. The proposed approach, RSPTE (Raman Spectroscopy Pre-Trained Encoder), is designed to learn generalizable spectral representations without labels. RSPTE employs a novel domain adaptation strategy using unsupervised Barlow Twins decorrelation objectives to learn fundamental spectral patterns from multi-domain Raman Spectroscopy datasets containing samples from medicine, biology, and mineralogy. Transferability is demonstrated through evaluation on several models created by fine-tuning RSPTE for different application domains: Medicine (detection of Melanoma and COVID), Biology (Pathogen Identification), and Agriculture. As an example, using only 20% of the dataset, models trained with RSPTE achieve accuracies ranging 50%–86% (depending on the dataset used) while without RSPTE the range is 9%–57%. Using the full dataset, accuracies with RSPTE range 81%–97%, and without pre-training 51%–97%. Current methods and state-of-the-art models in Raman Spectroscopy are compared to RSPTE for context, and RSPTE exhibits competitive results, especially with less data as well. These results provide evidence that the proposed RSPTE model can effectively learn and transfer generalizable spectral features across different domains, achieving accurate results with less data in less time (both data collection time and training time).