Human Herpes 1/2 IgA ELISA kit

Name: Human Herpes 1/2 IgA ELISA kit
SKU: DEIA351
Rating: 5 (1 reviews)

Regulatory status: For research use only, not for use in diagnostic procedures.

COA

SDS

Datasheet

Summary

Sample

serum, plasma or cerebrospinal fluid

Species Reactivity

Human

Detection Method

iELISA

Intended Use

Quantitative and qualitative tests for detection of human antibodies in serum, plasma or cerebrospinal fluid against Herpes Simplex Virus (HSV).

Contents of Kit

1.Break apart microtiter test strips each with 8 antigen coated single wells (altogether 96) MTP, 1 frame, the coating material is inactivated, 12
2.Standard serum (ready-to-use) STD Human serum in phosphate buffer with protein; negative for anti-HIV-Ab, HBs-Ag (Hepatitis B-Virus-surface antigen) and anti-HCV-Ab; preservative: < 0.1 % sodium azide, colouring: Amaranth O, 2 x 2 ml
3.Negative control serum (ready-to-use) NEG Human serum in phosphate buffer with protein; negative for anti-HIV-Ab, HBs-Ag (Hepatitis B-Virus-surface antigen) and anti-HCV; preservative: < 0.1 % sodium azide, colouring: Lissamin green V, 2 ml
4.Anti-human-IgA-conjugate (ready-to-use) APC Anti-human-IgA from goat (polyclonal), conjugated to alkaline phosphatase, stabilized with protein stabilization solution, preservative: 0.01 % methylisothiazolone, 0.01 % bromnitrodioxane, 13 ml
5.Washing solution concentrate (sufficient for 1 litre) WASH Sodium chloride solution with Tween 20, 30 mM Tris, preservative: < 0.1 % sodium azide, 33.3 ml
6.Dilution buffer DILB Phosphate buffer with protein and Tween 20; preservative: < 0.1 % sodium azide 0.01 g/l Bromphenol blue sodium salt, 2 x 50 ml
7.Stopping solution STOP 1.2 N sodium hydroxide, 15 ml
8.Substrate (ready-to-use) pNPP Para-nitrophenylphosphate, solvent free buffer, preservative: < 0.1 % sodium azide, (Substrate in unopened bottle may have a slightly yellow coloring. This does not reduce the quality of the product!), 13 ml
9.Quality control certificate with standard curve and evaluation table INFO (quantification of antibodies in IU/ml or U/ml), 1

Storage

General Description

Herpes Simplex Virus 1 (HSV1) and Herpes Simplex Virus 2 (HSV2) are human pathogen DNA viruses belonging to the family of Herpesviridae.
Herpes viruses are globally spread. Primary infection can be caused by contact with infected persons (saliva or urogenital secretion), cross-placentally or iatrogenically through transfusions or transplantations. After primary infection, local ganglions show lifelong virus persistence, enabling the reactivation of latent infections.
The transmission of Herpes genitalis during delivery can progress subclinically. In most cases it leads to localized or disseminated sepsis-like diseases.
After primary infection, viruses persist in certain cell types (latent cycle) until reactivation (lytic cycle). Reactivation occasionally progresses without symptoms or with low pathology in immunocompetent persons whereas severe clinical complications occur in immunocompromised patients.

Citations

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Documents

pdf COA

pdf SDS

pdf Datasheet

Infectious Disease Test Kits and Positive Controls

QA & Reviews

Customer Reviews

References

Discriminating crops/weeds in an upland rice field from UAV images with the SLIC-RF algorithm

PLANT PRODUCTION SCIENCE

Authors: Kawamura, Kensuke; Asai, Hidetoshi; Yasuda, Taisuke; Soisouvanh, Pheunphit; Phongchanmixay, Sengthong

Abstract

In this study, we propose a method for discriminating crops/weeds in upland rice fields using a commercial unmanned aerial vehicles (UAVs) and red-green-blue (RGB) cameras with the simple linear iterative clustering (SLIC) algorithm and random forest (RF) classifier. In the SLIC-RF algorithm, we evaluated different combinations of input features: three color spaces (RGB, hue-saturation-brightness [HSV], CIE-L*a*b), canopy height model (CHM), spatial texture (Texture) and four vegetation indices (VIs) (excess green [ExG], excess red [ExR], green-red vegetation index [GRVI] and color index of vegetation extraction [CIVE]). Among the color spaces, the HSV-based SLIC-RF model showed the best performance with the highest out-of-bag (OOB) accuracy (0.904). The classification accuracy was improved by the combination of HSV with CHM, Texture, ExG, or CIVE. The highest OOB accuracy (0.915) was obtained from the HSV+Texture combination. The greatest errors from the confusion matrix occurred in the classification between crops and weeds, while soil could be classified with a very high accuracy. These results suggest that with the SLIC-RF algorithm developed in this study, rice and weeds can be discriminated by consumer-grade UAV images with acceptable accuracy to meet the needs of site-specific weed management (SSWM) even in the early growth stages of small rice plants..

Sniffer-Net: quantitative evaluation of smoke in the wild based on spatial-temporal motion spectrum

NEURAL COMPUTING & APPLICATIONS

Authors: Mi, Zeyang; Zhang, Weiwei; Wu, Xuncheng; Gao, Qiaoming; Luo, Suyun

Abstract

Smoke detection plays an essential role in the wild video surveillance systems for abnormal events warning. In this paper, we introduced a dedicated neural network structure named Sniffer-Net to simultaneously extract smoke dynamic feature robustly and evaluate the smoke concentration accurately. Firstly, we utilize an improved LiteFlowNet to estimate the global optical flow from image sequence. Meanwhile, a Marr-Hildreth method is brought up and fused into this network to distinguish and eliminate occluded regions from global flow map. Then, an evaluation module based on Context-Encoder network is put forward specially to quantify smoke concentration levels. This network, following the improved LiteFlowNet, is modified through replacing the loss function and removing the multiscale scheme and trained to infer approximate smoke optical flow behind occlusion regions. Starting from the statistical view, the irregular RGB/HSV feature spaces are converted into a specific quantitative evaluation space. As a result, the whole evaluation system is responsible to transform the distribution of irregular smoke motion feature into a quantified form of representation. In turn, this transformation endows the system with a novel numerical standard for smoke concentration evaluation. Finally, an accuracy assessment method is applied to compare the results of detected smoke concentration with the human experience prior model, which feedback the accuracy and false detection rate of system algorithm. In the experiments of five smoke datasets, our proposed smoke detection approach is superior to other state-of-the-art methods, and concentration algorithm achieves the satisfactory performance of 97.3% accuracy on some specialized dataset.