scholarly journals Multispectral LIF-Based Standoff Detection System for the Classification of CBE Hazards by Spectral and Temporal Features

Sensors ◽  
2020 ◽  
Vol 20 (9) ◽  
pp. 2524 ◽  
Author(s):  
Lea Fellner ◽  
Marian Kraus ◽  
Florian Gebert ◽  
Arne Walter ◽  
Frank Duschek
Keyword(s):  
Spectral Data ◽  
Classification Accuracy ◽  
Detection System ◽  
Laser Source ◽  
Nanosecond Laser ◽  
Time Resolved Fluorescence ◽  
Data Set ◽  
Time Resolved ◽  
Standoff Detection

Laser-induced fluorescence (LIF) is a well-established technique for monitoring chemical processes and for the standoff detection of biological substances because of its simple technical implementation and high sensitivity. Frequently, standoff LIF spectra from large molecules and bio-agents are only slightly structured and a gain of deeper information, such as classification, let alone identification, might become challenging. Improving the LIF technology by recording spectral and additionally time-resolved fluorescence emission, a significant gain of information can be achieved. This work presents results from a LIF based detection system and an analysis of the influence of time-resolved data on the classification accuracy. A multi-wavelength sub-nanosecond laser source is used to acquire spectral and time-resolved data from a standoff distance of 3.5 m. The data set contains data from seven different bacterial species and six types of oil. Classification is performed with a decision tree algorithm separately for spectral data, time-resolved data and the combination of both. The first findings show a valuable contribution of time-resolved fluorescence data to the classification of the investigated chemical and biological agents to their species level. Temporal and spectral data have been proven as partly complementary. The classification accuracy is increased from 86% for spectral data only to more than 92%.

Sensitive, rapid procedure for time-resolved immunofluorometry of lutropin.

1988 ◽  
Vol 34 (8) ◽  
pp. 1640-1644 ◽  
Author(s):  
M J Khosravi ◽  
R C Morton ◽  
E P Diamandis
Keyword(s):  
Monoclonal Antibody ◽  
Solid Phase ◽  
Detection System ◽  
Time Resolved Fluorescence ◽  
Time Resolved ◽  
Practical Procedure ◽  
Daily Monitoring ◽  
Fluorescence Measurements ◽  
Capture Antibody ◽  
Europium Chelate

Abstract In this new immunofluorometric method for quantification of lutropin in serum, the "sandwich" principle is combined with time-resolved fluorescence measurements, with the europium chelate 4,7-bis(chlorosulfophenyl)-1,10-phenanthroline-2,9-dicarboxylic acid (BCPDA) used as label. A monoclonal antibody to the alpha-subunit of lutropin is adsorbed onto the walls of white-opaque microtiter wells to form the solid-phase capture antibody, and a biotin-labeled soluble monoclonal antibody is used for antigen quantification. The detection system is completed with streptavidin, which has been linked to a protein bulking agent labeled with multiple BCPDA residues. In the presence of excess europium, the fluorescence of the final complex attached to captured lutropin molecules is measured on the dried solid phasse with an automated time-resolved fluorometer. The assay can be performed as a rapid (less than 60 min incubation) or regular (150 min incubation) procedure. The rapid assay is well-suited for routine daily monitoring of increasing or ovulatory lutropin concentrations; the regular assay, with its greater sensitivity (0.5 int. unit/L), is a practical procedure for lutropin measurements in hyposecretory states. The assay measures up to 240 int. units/L, and results compare well with those by a commercially available radioimmunoassay, an immunoradiometric assay, and another time-resolved immunofluorometric procedure.

scholarly journals Deep Stacking Network for Intrusion Detection

Sensors ◽  
2021 ◽  
Vol 22 (1) ◽  
pp. 25
Author(s):  
Yifan Tang ◽  
Lize Gu ◽  
Leiting Wang
Keyword(s):  
Intrusion Detection ◽  
Decision Tree ◽  
Classification Accuracy ◽  
Intrusion Detection System ◽  
Detection System ◽  
Detection Performance ◽  
Network Intrusion Detection ◽  
Fusion Model ◽  
Data Set ◽  
Network Intrusion

Preventing network intrusion is the essential requirement of network security. In recent years, people have conducted a lot of research on network intrusion detection systems. However, with the increasing number of advanced threat attacks, traditional intrusion detection mechanisms have defects and it is still indispensable to design a powerful intrusion detection system. This paper researches the NSL-KDD data set and analyzes the latest developments and existing problems in the field of intrusion detection technology. For unbalanced distribution and feature redundancy of the data set used for training, some training samples are under-sampling and feature selection processing. To improve the detection effect, a Deep Stacking Network model is proposed, which combines the classification results of multiple basic classifiers to improve the classification accuracy. In the experiment, we screened and compared the performance of various mainstream classifiers and found that the four models of the decision tree, k-nearest neighbors, deep neural network and random forests have outstanding detection performance and meet the needs of different classification effects. Among them, the classification accuracy of the decision tree reaches 86.1%. The classification effect of the Deeping Stacking Network, a fusion model composed of four classifiers, has been further improved and the accuracy reaches 86.8%. Compared with the intrusion detection system of other research papers, the proposed model effectively improves the detection performance and has made significant improvements in network intrusion detection.

Discrimination of Storage Duration of Apples Stored in a Cooled Room and Shelf-Life by Visible-Near Infrared Spectroscopy

2007 ◽  
Vol 15 (3) ◽  
pp. 169-177 ◽  
Author(s):  
C. Camps ◽  
P. Guillermin ◽  
J.C. Mauget ◽  
D. Bertrand
Keyword(s):  
Spectral Data ◽  
Near Infrared ◽  
Nir Spectroscopy ◽  
Absorption Bands ◽  
Storage Duration ◽  
Data Set ◽  
Global Models ◽  
Duration Of Storage ◽  
Apple Cultivars

Improved non-destructive instrumental approaches for grading fruit during post-harvest could be an efficient way to monitor stock in the apple industry. The objective of this study was to evaluate the ability of visible-near infrared (vis-NIR) spectroscopy in reflectance mode for classifying apples left on the shelf or stored in a cooled room. The ability of NIR spectroscopy to classify the duration of storage of three apple cultivars in two storage modalities was evaluated. A total of 450 fruit, sampled after 7, 14, 28, 60, 90 and 120 days of storage in a cooled room (CR) and 7, 14 and 28 days in shelflife (SL), has been studied. The classification of these modalities was analysed by factorial discriminant analysis (FDA) pooling the spectral data of all cultivars (global models) into a common data set. Then, the cultivar effect on the classification of the same modalities was analysed by processing data from each cultivar in separate factorial descriminant analyses. A preliminary analysis showed the genetic variability of spectral data due to the three apple cultivars. We show that vis-NIR spectroscopy allowed the correct classification of the fruits of each cultivar by more than 95%. The classification relied on both vis and NIR absorption bands: 500, 680, 1400 to 1700, 1850, 1950, 2200 and 2300 nm. We show that storage modalities of global models can be classified by more than 75% and 83% for fruits stored in a cooled room and shelf, respectively. Classification of the same storage modalities was improved by cultivar models with percentage of individuals correctly classified of 86% (Gala), 89% (Elstar) and 85% (Smoothee) for fruits stored in a cooled room and 95% (Gala), 98% (Elstar) and 95% (Smoothee) for fruits left in shelflife. We conclude that despite the slight increase of efficiency of the models when we considered each apple cultivar separately, global models applicable to a set of different cultivars presents a correct level of classification and could be usefull for some commercial applications.

scholarly journals Identification of Small-Molecule Frequent Hitters from AlphaScreen High-Throughput Screens

2013 ◽  
Vol 19 (5) ◽  
pp. 715-726 ◽  
Author(s):  
Kenji Schorpp ◽  
Ina Rothenaigner ◽  
Elena Salmina ◽  
Jeanette Reinshagen ◽  
Terence Low ◽  
...  
Keyword(s):  
Small Molecule ◽  
Detection System ◽  
Resonance Energy Transfer ◽  
Resonance Energy ◽  
Binding Assays ◽  
Time Resolved ◽  
Protein Protein Interaction ◽  
Nickel Chelate ◽  
High Throughput Screens

Although small-molecule drug discovery efforts have focused largely on enzyme, receptor, and ion-channel targets, there has been an increase in such activities to search for protein-protein interaction (PPI) disruptors by applying high-throughout screening (HTS)–compatible protein-binding assays. However, a disadvantage of these assays is that many primary hits are frequent hitters regardless of the PPI being investigated. We have used the AlphaScreen technology to screen four different robust PPI assays each against 25,000 compounds. These activities led to the identification of 137 compounds that demonstrated repeated activity in all PPI assays. These compounds were subsequently evaluated in two AlphaScreen counter assays, leading to classification of compounds that either interfered with the AlphaScreen chemistry (60 compounds) or prevented the binding of the protein His-tag moiety to nickel chelate (Ni2+-NTA) beads of the AlphaScreen detection system (77 compounds). To further triage the 137 frequent hitters, we subsequently confirmed by a time-resolved fluorescence resonance energy transfer assay that most of these compounds were only frequent hitters in AlphaScreen assays. A chemoinformatics analysis of the apparent hits provided details of the compounds that can be flagged as frequent hitters of the AlphaScreen technology, and these data have broad applicability for users of these detection technologies.

scholarly journals An Automatic System for Atrial Fibrillation by Using a CNN-LSTM Model

2020 ◽  
Vol 2020 ◽  
pp. 1-9
Author(s):  
Fengying Ma ◽  
Jingyao Zhang ◽  
Wei Chen ◽  
Wei Liang ◽  
Wenjia Yang
Keyword(s):  
Atrial Fibrillation ◽  
Classification Accuracy ◽  
Short Term Memory ◽  
Sequence Data ◽  
Detection System ◽  
Heart Rhythm ◽  
Classification Performance ◽  
Ecg Signals ◽  
Electrocardiogram Ecg

Atrial fibrillation (AF) is a common abnormal heart rhythm disease. Therefore, the development of an AF detection system is of great significance to detect critical illnesses. In this paper, we proposed an automatic recognition method named CNN-LSTM to automatically detect the AF heartbeats based on deep learning. The model combines convolutional neural networks (CNN) to extract local correlation features and uses long short-term memory networks (LSTM) to capture the front-to-back dependencies of electrocardiogram (ECG) sequence data. The CNN-LSTM is feeded by processed data to automatically detect AF signals. Our study uses the MIT-BIH Atrial Fibrillation Database to verify the validity of the model. We achieved a high classification accuracy for the heartbeat data of the test set, with an overall classification accuracy rate of 97.21%, sensitivity of 97.34%, and specificity of 97.08%. The experimental results show that our model can robustly detect the onset of AF through ECG signals and achieve stable classification performance, thereby providing a suitable candidate for the automatic classification of AF.

scholarly journals Solid-Phase PCR with Hybridization and Time-resolved Fluorometry for Detection of HLA-B27

2001 ◽  
Vol 47 (3) ◽  
pp. 498-504 ◽  
Author(s):  
Minna Sjöroos ◽  
Jorma Ilonen ◽  
Timo Lövgren
Keyword(s):  
Solid Phase ◽  
Detection System ◽  
Pcr Primers ◽  
Dried Blood Spots ◽  
Microtiter Plate ◽  
Covalent Immobilization ◽  
Hla B27 ◽  
Time Resolved Fluorescence ◽  
Time Resolved ◽  
Consecutive Reactions

Abstract Background: Preactivated solid surfaces provide new possibilities for multiple consecutive reactions in a microtiter plate format. In this study, a combination of PCR and subsequent hybridization in the same microtiter well was applied for the detection of HLA-B27 alleles. Methods: A multiplex solid-phase PCR to amplify the HLA-B27 alleles together with β-actin as an amplification control gene was performed on the NucleoLinkTM (Nunc) surface. PCR was followed by hybridization and detection with time-resolved fluorescence. For the covalent capture of the PCR primers onto the solid support via a 1-(3-dimethylamino-propyl)-3-ethylcarbodiimide hydrochloride-mediated reaction, different 5′-end modifications of oligonucleotides were tested [amination, phosphorylation, and a poly(dT)10 linker]. Results: For covalent immobilization of the primers, amination of the 5′ end combined with use of the poly(dT)10 linker was superior. At least 19.5% of the primer added per well was attached via a stable bond. When the standard time-resolved, fluorescence-based HLA-B27 detection system was compared with the newly developed method in a sample series of 82 genomic DNAs and the corresponding dried-blood spots, all results were in full agreement. Conclusions: The new solid-phase PCR approach can be applied for multiple-target DNA detection. PCR followed by hybridization can be accomplished in a few hours using precoated strips and dried-blood spot PCR templates.

scholarly journals Classification of caries in third molars on panoramic radiographs using deep learning

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Shankeeth Vinayahalingam ◽  
Steven Kempers ◽  
Lorenzo Limon ◽  
Dionne Deibel ◽  
Thomas Maal ◽  
...  
Keyword(s):  
Deep Learning ◽  
Classification Accuracy ◽  
Third Molar ◽  
Area Under The Curve ◽  
Third Molars ◽  
Data Set ◽  
Panoramic Radiographs ◽  
Carious Lesions ◽  
Panoramic Images

AbstractThe objective of this study is to assess the classification accuracy of dental caries on panoramic radiographs using deep-learning algorithms. A convolutional neural network (CNN) was trained on a reference data set consisted of 400 cropped panoramic images in the classification of carious lesions in mandibular and maxillary third molars, based on the CNN MobileNet V2. For this pilot study, the trained MobileNet V2 was applied on a test set consisting of 100 cropped PR(s). The classification accuracy and the area-under-the-curve (AUC) were calculated. The proposed method achieved an accuracy of 0.87, a sensitivity of 0.86, a specificity of 0.88 and an AUC of 0.90 for the classification of carious lesions of third molars on PR(s). A high accuracy was achieved in caries classification in third molars based on the MobileNet V2 algorithm as presented. This is beneficial for the further development of a deep-learning based automated third molar removal assessment in future.

scholarly journals Research on the classification of lymphoma pathological images based on deep residual neural network

2021 ◽  
Vol 29 ◽  
pp. 335-344
Author(s):  
Xiaoli Zhang ◽  
Kuixing Zhang ◽  
Mei Jiang ◽  
Lin Yang
Keyword(s):  
Neural Network ◽  
Experimental Data ◽  
Network Model ◽  
Bp Neural Network ◽  
Classification Accuracy ◽  
Data Set ◽  
Color Transformation ◽  
Objective Basis ◽  
Time And Energy

BACKGROUND: Malignant lymphoma is a type of tumor that originated from the lymphohematopoietic system, with complex etiology, diverse pathological morphology, and classification. It takes a lot of time and energy for doctors to accurately determine the type of lymphoma by observing pathological images. OBJECTIVE: At present, an automatic classification technology is urgently needed to assist doctors in analyzing the type of lymphoma. METHODS: In this paper, by comparing the training results of the BP neural network and BP neural network optimized by genetic algorithm (GA-BP), adopts a deep residual neural network model (ResNet-50), with 374 lymphoma pathology images as the experimental data set. After preprocessing the dataset by image flipping, color transformation, and other data enhancement methods, the data set is input into the ResNet-50 network model, and finally classified by the softmax layer. RESULTS: The training results showed that the classification accuracy was 98.63%. By comparing the classification effect of GA-BP and BP neural network, the accuracy of the network model proposed in this paper is improved. CONCLUSIONS: The network model can provide an objective basis for doctors to diagnose lymphoma types.

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