scholarly journals On Single-Class Load-Dependent Normalizing Constant Equations

2006 ◽  
Keyword(s):  
Single Class ◽  
Normalizing Constant

Modulation of Platelet Activation by Native DNA – Initial Description of Platelet Receptor Type, Number and Discrimination for Native DNA

1981 ◽  
Vol 45 (03) ◽  
pp. 263-266 ◽  
Author(s):  
B A Fiedel ◽  
M E Frenzke
Keyword(s):  
Platelet Aggregation ◽  
Human Platelets ◽  
Scatchard Analysis ◽  
Type Number ◽  
Single Class ◽  
Receptor Ligand ◽  
Platelet Receptor ◽  
Platelet Binding ◽  
Ligand Interactions ◽  
Initial Description

SummaryNative DNA (dsDNA) induces the aggregation of isolated human platelets. Using isotopically labeled dsDNA (125I-dsDNA) and Scatchard analysis, a single class of platelet receptor was detected with a KD = 190 pM and numbering ~275/platelet. This receptor was discriminatory in that heat denatured dsDNA, poly A, poly C, poly C · I and poly C · poly I failed to substantially inhibit either the platelet binding of, or platelet aggregation induced by, dsDNA; by themselves, these polynucleotides were ineffective as platelet agonists. However, poly G, poly I and poly G · I effectively and competitively inhibited platelet binding of the radioligand, independently activated the platelet and when used at a sub-activating concentration decreased the extent of dsDNA stimulated platelet aggregation. These data depict a receptor on human platelets for dsDNA and perhaps certain additional polynucleotides and relate receptor-ligand interactions to a physiologic platelet function.

Characterization of Thromboxane A2/Prostaglandin H2 Receptors in Porcine Coronary Artery -The Inhibitory Effect of a Novel Dibenzoxepin Derivative, KW-3635

1992 ◽  
Vol 67 (05) ◽  
pp. 582-584 ◽  
Author(s):  
Ichiro Miki ◽  
Akio Ishii
Keyword(s):  
Coronary Artery ◽  
Binding Sites ◽  
Thromboxane A2 ◽  
Inhibitory Effect ◽  
Scatchard Analysis ◽  
Single Class ◽  
Porcine Coronary Artery ◽  
Equilibrium Binding ◽  
H2 Receptors

SummaryWe characterized the thromboxane A2/prostaglandin H2 receptors in porcine coronary artery. The binding of [3H]SQ 29,548, a thromboxane A2 antagonist, to coronary arterial membranes was saturable and displaceable. Scatchard analysis of equilibrium binding showed a single class of high affinity binding sites with a dissociation constant of 18.5 ±1.0 nM and the maximum binding of 80.7 ± 5.2 fmol/mg protein. [3H]SQ 29,548 binding was concentration-dependently inhibited by thromboxane A2 antagonists such as SQ 29,548, BM13505 and BM13177 or the thromboxane A2 agonists such as U46619 and U44069. KW-3635, a novel dibenzoxepin derivative, concentration-dependently inhibited the [3H]SQ 29,548 binding to thromboxane A2/prosta-glandin H2 receptors in coronary artery with an inhibition constant of 6.0 ± 0.69 nM (mean ± S.E.M.).

Unsupervised Fault Detection in Refrigeration Showcase with Single Class Data using Autoencoders

2019 ◽  
Vol 139 (10) ◽  
pp. 1191-1200 ◽  
Author(s):  
Adamo Santana ◽  
Yu Kawamura ◽  
Kenya Murakami ◽  
Tatsuya Iizaka ◽  
Tetsuro Matsui ◽  
...  
Keyword(s):  
Fault Detection ◽  
Single Class

Networks, Status, and Inequality

2020 ◽  
pp. 97-115
Author(s):  
John Levi Martin ◽  
James P. Murphy
Keyword(s):  
Network Data ◽  
Organizational Boundaries ◽  
Single Class ◽  
Cultural Products ◽  
The Status

The notion that there is a single class of objects, “networks,” has been a great inspiration to new forms of structural thinking. Networks are considered to be a set of largely voluntary ties that often span organizational boundaries. Despite being divorced from formal hierarchies, they make possible other forms of differentiation, such as status. It is common for network data to be used to produce measures of the status of the nodes (individuals, organizations, cultural products, etc.) and the distribution of these statuses to describe a backdrop of inequality that may condition action or other processes. However, it is also important that network researchers understand the backdrop of various forms of potential inequality that may condition the collection of network data.

scholarly journals U-Infuse: Democratization of Customizable Deep Learning for Object Detection

Sensors ◽  
2021 ◽  
Vol 21 (8) ◽  
pp. 2611
Author(s):  
Andrew Shepley ◽  
Greg Falzon ◽  
Christopher Lawson ◽  
Paul Meek ◽  
Paul Kwan
Keyword(s):  
Deep Learning ◽  
Intellectual Property ◽  
Object Detection ◽  
Image Data ◽  
Learning Technologies ◽  
Training Data ◽  
Learning Models ◽  
Ecological Data ◽  
Single Class ◽  
Large Numbers

Image data is one of the primary sources of ecological data used in biodiversity conservation and management worldwide. However, classifying and interpreting large numbers of images is time and resource expensive, particularly in the context of camera trapping. Deep learning models have been used to achieve this task but are often not suited to specific applications due to their inability to generalise to new environments and inconsistent performance. Models need to be developed for specific species cohorts and environments, but the technical skills required to achieve this are a key barrier to the accessibility of this technology to ecologists. Thus, there is a strong need to democratize access to deep learning technologies by providing an easy-to-use software application allowing non-technical users to train custom object detectors. U-Infuse addresses this issue by providing ecologists with the ability to train customised models using publicly available images and/or their own images without specific technical expertise. Auto-annotation and annotation editing functionalities minimize the constraints of manually annotating and pre-processing large numbers of images. U-Infuse is a free and open-source software solution that supports both multiclass and single class training and object detection, allowing ecologists to access deep learning technologies usually only available to computer scientists, on their own device, customised for their application, without sharing intellectual property or sensitive data. It provides ecological practitioners with the ability to (i) easily achieve object detection within a user-friendly GUI, generating a species distribution report, and other useful statistics, (ii) custom train deep learning models using publicly available and custom training data, (iii) achieve supervised auto-annotation of images for further training, with the benefit of editing annotations to ensure quality datasets. Broad adoption of U-Infuse by ecological practitioners will improve ecological image analysis and processing by allowing significantly more image data to be processed with minimal expenditure of time and resources, particularly for camera trap images. Ease of training and use of transfer learning means domain-specific models can be trained rapidly, and frequently updated without the need for computer science expertise, or data sharing, protecting intellectual property and privacy.

When Seeing Is Believing: Generalizability and Decision Studies for Observational Data in Evaluation and Research on Teaching

2021 ◽  
pp. 109821402093194
Author(s):  
Timothy J. Weston ◽  
Charles N. Hayward ◽  
Sandra L. Laursen
Keyword(s):  
Teaching Practices ◽  
Interrater Reliability ◽  
Teaching And Learning ◽  
Generalizability Theory ◽  
Learning Activities ◽  
Observation Data ◽  
Reliable Measure ◽  
Single Class ◽  
Research On Teaching ◽  
The One

Observations are widely used in research and evaluation to characterize teaching and learning activities. Because conducting observations is typically resource intensive, it is important that inferences from observation data are made confidently. While attention focuses on interrater reliability, the reliability of a single-class measure over the course of a semester receives less attention. We examined the use and limitations of observation for evaluating teaching practices, and how many observations are needed during a typical course to make confident inferences about teaching practices. We conducted two studies based on generalizability theory to calculate reliabilities given class-to-class variation in teaching over a semester. Eleven observations of class periods over the length of a semester were needed to achieve a reliable measure, many more than the one to four class periods typically observed in the literature. Findings suggest practitioners may need to devote more resources than anticipated to achieve reliable measures and comparisons.

scholarly journals A Note on Equivalence Classes of Directed Acyclic Independence Graphs

1993 ◽  
Vol 7 (3) ◽  
pp. 409-412 ◽  
Author(s):  
David Madigan
Keyword(s):  
Model Selection ◽  
Expert Systems ◽  
Equivalence Classes ◽  
Influence Diagrams ◽  
Single Class ◽  
Selection Procedures ◽  
Probabilistic Expert Systems ◽  
Recent Developments ◽  
Independence Graphs ◽  
Markov Properties

Directed acyclic independence graphs (DAIGs) play an important role in recent developments in probabilistic expert systems and influence diagrams (Chyu [1]). The purpose of this note is to show that DAIGs can usefully be grouped into equivalence classes where the members of a single class share identical Markov properties. These equivalence classes can be identified via a simple graphical criterion. This result is particularly relevant to model selection procedures for DAIGs (see, e.g., Cooper and Herskovits [2] and Madigan and Raftery [4]) because it reduces the problem of searching among possible orientations of a given graph to that of searching among the equivalence classes.

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