Rabbit Anti-Human EMD (a.a. 66-252) Polyclonal antibody

Name: Rabbit Anti-Human EMD (a.a. 66-252) Polyclonal antibody
SKU: DPABH-05828
Rating: 5 (1 reviews)

Rabbit Anti-Human EMD (a.a. 66-252) Polyclonal antibody for ELISA, IHC, WB

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COA

SDS

Datasheet

Key features and details

Target: Human EMD
Host: Rabbit
Application: ELISA, IHC, WB
Species Reactivity: Human

Summary

Specifications

Antibody Isotype

IgG

Species Reactivity

Human

Immunogen

E. coli - derived recombinant Human EMD (Ser66-Asn252).

Conjugate

Unconjugated

Applications

Application Notes

ELISA: 1:4000-1:8000
IHC: 1:50-1:100
WB: 1:1000-1:4000

General Notes

This product (Catalog No. DPABH-05828) is a rabbit-derived polyclonal antibody raised against human EMD, targeting the amino acid region 66–252 of the protein. It has been affinity purified by Protein A. Emerin is a protein that in humans is encoded by the EMD gene, also known as the STA gene. Emerin, together with LEMD3, is a LEM domain-containing integral protein of the inner nuclear membrane in vertebrates. Emerin is highly expressed in cardiac and skeletal muscle. In cardiac muscle, emerin localizes to adherens junctions within intercalated discs where it appears to function in mechanotransduction of cellular strain and in beta-catenin signaling. Mutations in emerin cause X-linked recessive Emery–Dreifuss muscular dystrophy, cardiac conduction abnormalities and dilated cardiomyopathy.
DPABH-05828 is suitable for use in ELISA (1:4,000–1:8,000), IHC (1:50–1:100), Western blot (WB) (1:1,000–1:4,000) applications.
DPABH-05828 is supplied as a liquid formulation in 0.01M PBS, pH 7.4, 50% Glycerol, 0.05% Proclin 300. For long-term storage, keep at −20 °C. For short-term use, store at 2–8 °C. Avoid repeated freeze–thaw cycles to maintain antibody integrity and performance.

Target

Alternative Names

EMD; EDMD; STA; Emerin

UniProt ID

P50402

Citations

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Documents

pdf COA

pdf SDS

pdf Datasheet

QA & Reviews

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References

Pseudo-pulseless electrical activity in the emergency department, an evidence based approach

AMERICAN JOURNAL OF EMERGENCY MEDICINE

Authors: Rabjohns, J.; Quan, T.; Boniface, K.; Pourmand, A.

Abstract

Introduction: A great deal of the literature has focused specifically on true pulseless electrical activity (PEA), whereas there is a dearth of research regarding pseudo-PEA. This narrative review evaluates the diagnosis and management of patients in pseudo-PEA and discusses the impact on emerging patient outcomes. Discussion: Pseudo-PEA can be defined as evidence of cardiac activity without a detectable pulse. Distinguishing pseudo-PEA from true PEA is important for emergency physicians as the prognosis and management of these patients differ. POCUS is the tool most commonly used to diagnose pseudo-PEA and there are varying treatment strategies to manage these patients. Identifying patients in pseudo-PEA can help guide resuscitation decisions, and ultimately impact emergency response systems, patients, and families. Conclusions: The incidence of pseudo-PEA is increasing. Effective care of these patients begins with early diagnosis of this condition and immediate treatment to warrant the greatest chance of survival. There is a need for further prospective studies surrounding pseudo-PEA as evidenced by the lack of research in the current literature. (C) 2019 Elsevier Inc. All rights reserved.

An adaptive training-less framework for anomaly detection in crowd scenes

NEUROCOMPUTING

Authors: Sikdar, Arindam; Chowdhury, Ananda S.

Abstract

Anomaly detection in crowd videos has become a popular area of research for the computer vision community. Several existing methods have determined anomaly as a deviation from scene normalcy learned via separate training with/without labeled information. However, owing to rare and sparse nature of anomalous events, any such learning can be misleading as there exist no hardcore segregation between anomalous and non-anomalous events. To address such challenge, we propose an adaptive training-less system capable of detecting anomaly on-the-fly. Our solution pipeline consists of three major components, namely, adaptive 3D-DCT model for multi-object detection-based association, local motion descriptor generation through an improved saliency guided optical flow, and anomaly detection based on Earth mover's distance (EMD). The proposed model, despite being training-free, is found to achieve comparable performance with several state-of-the-art methods on publicly available UCSD, UMN, CUHK-Avenue and ShanghaiTech datasets. (C) 2020 Elsevier B.V. All rights reserved.