Polyamine and Histamine Content of Rockfish, Salmon, Lobster, and Shrimp as an Indicator of Decomposition

1978 ◽  
Vol 61 (1) ◽  
pp. 139-145 ◽  
Author(s):  
John L Mietz ◽  
Endel Karmas
Keyword(s):  
Chemical Method ◽  
Gradient Elution ◽  
Index Formula ◽  
Correct Classification ◽  
Similar Relationship ◽  
Chemical Index ◽  
Class 1 ◽  
Dansyl Derivatives ◽  
Decomposition Class

Abstract A chemical method to determine decomposition in rockfish, salmon, lobster, and shrimp was developed. Dansyl derivatives were formed from extracts of these products and separated using gradient elution high pressure liquid chromatography. The dansylated polyamines, putrescine, cadaverine, spermidine, and spermine, and the amine histamine were quantitated, and the results were entered into an index formula. The resulting index was used to classify each product into passable (class 1), initial decomposition (class 2), and advanced decomposition (class 3). A comparison of results obtained by the chemical classification of 21 samples (rockfish, salmon, and lobster) showed good correlation with organoleptic evaluations of the same products by 23 examiners. The chemical index classified the samples correctly 90.5% of the time vs. an 83.9% correct classification by organoleptic means. Analysis of shrimp composites of the 3 organoleptic classes showed a similar relationship between the chemical index and the degree of decomposition.

scholarly journals Coaxial Access for Microcatheter Stabilization During Aneurysm Coiling: 2-Dimensional Operative Video

2021 ◽  
Author(s):  
Miri Kim ◽  
Rachyl Shanker ◽  
Anthony Kam ◽  
Matthew Reynolds ◽  
Joseph C Serrone
Keyword(s):  
Artery Aneurysm ◽  
Anterior Communicating Artery ◽  
Distal Catheter ◽  
Anterior Communicating Artery Aneurysm ◽  
Class 1 ◽  
Previously Treated ◽  
Aneurysm Occlusion ◽  
Intracranial Circulation ◽  
Aneurysm Coiling

Abstract Coaxial support is a fundamental technique utilized by neurointerventionalists to optimize distal catheter control within the intracranial circulation. Here we present a 41-yr-old woman with a previously coiled ruptured anterior communicating artery aneurysm with progressive recurrence harboring tortuous internal carotid anatomy to demonstrate the utility of coaxial support. Raymond-Roy classification of initial aneurysm coiling of class 1 resulted as class 3b over the 21 mo from initial treatment.1 The patient consented to stent-assisted coiling for retreatment of this aneurysm. Coaxial support was advanced as distally as possible in the proximal vasculature to improve catheter control, reducing dead space within which the microcatheter could move, decreasing angulations within proximal vasculature, limiting the movement of the native vessels, and providing a surface of lower friction than the endothelium. As the risk of recurrent subarachnoid hemorrhage in previously treated coiled aneurysms approaches 3%, retreatment occurs in 16.4% within 6 yr2 and in 17.4% of patients within 10 yr.3 Rerupture is slightly higher in patients who underwent coiling vs clipping, with the rerupture risk inversely proportional to the degree of aneurysm occlusion,4 further substantiating that coaxial support provides technical advantage in selected patients where additional microcatheter control is necessary for optimal occlusion. Pitfalls of this technique include vasospasm and vascular injury, which can be ameliorated by pretreatment of the circulation with vasodilators to prevent catheter-induced vasospasm. This case and model demonstration illustrates the technique of coaxial access in the stent-assisted coiling of a recurrent anterior communicating artery aneurysm and identification and management of catheter-induced vasospasm.

Classification of EEG Mental Patterns by Using Two Scalp Electrodes and Mahalanobis Distance-Based Classifiers

2002 ◽  
Vol 41 (04) ◽  
pp. 337-341 ◽  
Author(s):  
F. Cincotti ◽  
D. Mattia ◽  
C. Babiloni ◽  
F. Carducci ◽  
L. Bianchi ◽  
...  
Keyword(s):  
Mahalanobis Distance ◽  
Brain Computer Interface ◽  
Computer Interface ◽  
Correct Classification ◽  
Interface Area ◽  
System A ◽  
The Brain ◽  
Full Covariance Matrix ◽  
Recording Electrodes

Summary Objectives: In this paper, we explored the use of quadratic classifiers based on Mahalanobis distance to detect mental EEG patterns from a reduced set of scalp recording electrodes. Methods: Electrodes are placed in scalp centro-parietal zones (C3, P3, C4 and P4 positions of the international 10-20 system). A Mahalanobis distance classifier based on the use of full covariance matrix was used. Results: The quadratic classifier was able to detect EEG activity related to imagination of movement with an affordable accuracy (97% correct classification, on average) by using only C3 and C4 electrodes. Conclusions: Such a result is interesting for the use of Mahalanobis-based classifiers in the brain computer interface area.

Reliability of equine visual lameness classification as a function of expertise, lameness severity and rater confidence

2018 ◽  
Vol 184 (2) ◽  
pp. 63-63 ◽  
Author(s):  
Sandra Dorothee Starke ◽  
Maarten Oosterlinck
Keyword(s):  
Diagnostic Accuracy ◽  
Three Dimensional ◽  
Chance Level ◽  
Correct Classification ◽  
Full Understanding ◽  
Case Load ◽  
Movement Asymmetry ◽  
Load Exposure ◽  
Hindlimb Lameness

Visual equine lameness assessment is often unreliable, yet the full understanding of this issue is missing. Here, we investigate visual lameness assessment using near-realistic, three-dimensional horse animations presenting with 0–60 per cent movement asymmetry. Animations were scored at an equine veterinary seminar by attendees with various expertise levels. Results showed that years of experience and exposure to a low, medium or high case load had no significant effect on correct assessment of lame (P>0.149) or sound horses (P≥0.412), with the exception of a significant effect of case load exposure on forelimb lameness assessment at 60 per cent asymmetry (P=0.014). The correct classification of sound horses as sound was significantly (P<0.001) higher for forelimb (average 72 per cent correct) than for hindlimb lameness assessment (average 28 per cent correct): participants often saw hindlimb lameness where there was none. For subtle lameness, errors often resulted from not noticing forelimb lameness and from classifying the incorrect limb as lame for hindlimb lameness. Diagnostic accuracy was at or below chance level for some metrics. Rater confidence was not associated with performance. Visual gait assessment may overall be unlikely to reliably differentiate between sound and mildly lame horses irrespective of an assessor’s background.

Alzheimer's Disease Classification Based on Multi-feature Fusion

2019 ◽  
Vol 15 (2) ◽  
pp. 161-169 ◽  
Author(s):  
Nuwan Madusanka ◽  
Heung-Kook Choi ◽  
Jae-Hong So ◽  
Boo-Kyeong Choi
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Feature Fusion ◽  
Biological Data ◽  
Correct Classification ◽  
Mr Images ◽  
Correct Classification Rate ◽  
Classification Rate ◽  
Morphometric Features

Background: In this study, we investigated the fusion of texture and morphometric features as a possible diagnostic biomarker for Alzheimer’s Disease (AD). Methods: In particular, we classified subjects with Alzheimer’s disease, Mild Cognitive Impairment (MCI) and Normal Control (NC) based on texture and morphometric features. Currently, neuropsychiatric categorization provides the ground truth for AD and MCI diagnosis. This can then be supported by biological data such as the results of imaging studies. Cerebral atrophy has been shown to correlate strongly with cognitive symptoms. Hence, Magnetic Resonance (MR) images of the brain are important resources for AD diagnosis. In the proposed method, we used three different types of features identified from structural MR images: Gabor, hippocampus morphometric, and Two Dimensional (2D) and Three Dimensional (3D) Gray Level Co-occurrence Matrix (GLCM). The experimental results, obtained using a 5-fold cross-validated Support Vector Machine (SVM) with 2DGLCM and 3DGLCM multi-feature fusion approaches, indicate that we achieved 81.05% ±1.34, 86.61% ±1.25 correct classification rate with 95% Confidence Interval (CI) falls between (80.75-81.35) and (86.33-86.89) respectively, 83.33%±2.15, 84.21%±1.42 sensitivity and 80.95%±1.52, 85.00%±1.24 specificity in our classification of AD against NC subjects, thus outperforming recent works found in the literature. For the classification of MCI against AD, the SVM achieved a 76.31% ± 2.18, 78.95% ±2.26 correct classification rate, 75.00% ±1.34, 76.19%±1.84 sensitivity and 77.78% ±1.14, 82.35% ±1.34 specificity. Results and Conclusion: The results of the third experiment, with MCI against NC, also showed that the multiclass SVM provided highly accurate classification results. These findings suggest that this approach is efficient and may be a promising strategy for obtaining better AD, MCI and NC classification performance.

scholarly journals Application of neuron networks of deep learning for exposures editing of digital phonograms

2020 ◽  
Vol 65 (4) ◽  
pp. 506-512
Author(s):  
V. I. Solovyov ◽  
O. V. Rybalskiy ◽  
V. V. Zhuravel ◽  
V. K. Zheleznyak
Keyword(s):  
Deep Learning ◽  
Binary Classification ◽  
Maximum Size ◽  
Maximum Efficiency ◽  
Correct Classification ◽  
Digital Audio ◽  
Neuron Networks ◽  
Effective System ◽  
Further Development

Possibility of creation of effective system, which is intended for exposure of tracks of editing in digital phonograms and is built on the basis of neuron networks of the deep learning, is experimentally proven. Sense of experiment consisted in research of ability of the systems on the basis of such networks to expose pauses with tracks of editing. The experimental array of data is created in a voice editor from phonograms written on the different apparatus of the digital audio recording (at frequency of discretisation 44,1 kHz). A preselection of pauses was produced from it, having duration from 100 мs to a few seconds. From 1000 selected pauses the array of fragments of pauses is formed in the automatic (computer) mode, from which the arrays of fragments of pauses of different duration are generated by a dimension about 100 000. For forming of array of fragments of pauses with editing, the chosen pauses were divided into casual character parts in arbitrary correlation. Afterwards, the new pauses were created from it with the fixed place of editing. The general array of all fragments of pauses was broken into training and test arrays. The maximum efficiency, achieved on a test array in the process of educating, was determined. In general case this efficiency is determined by the maximum size of probability of correct classification of fragments with editing and fragments without editing. Scientifically reasonable methodology of exposure of signs of editing in digital phonograms is offered on the basis of neuron networks of the deep learning. The conducted experiments showed that the construction of the effective system is possible for the exposure of such tracks. Further development of methodology must be directed to find the ways to increase the probability of correct binary classification of investigated pauses.

scholarly journals A Functioning Model of a Dual-Band Radar System That Classifies Targets under the Influence of Interference and Noise

2020 ◽  
Vol 6 (4) ◽  
pp. 112-119
Author(s):  
V. Makarenkov
Keyword(s):  
Gaussian Noise ◽  
Radar System ◽  
White Gaussian Noise ◽  
Dual Band ◽  
Correct Classification ◽  
Noise Interference ◽  
Active Noise ◽  
Complex Target

There is proposed a model of a signal received from a complex target formed by a set of rapidly fluctuating point reflectors. Signal reception is carried out against the background of narrow-broadband active noise interference and white Gaussian noise. A functioning model of a dual-band radar system is proposed, in which the problem of classifying rapidly fluctuating point reflectors as a part of complex target against the background of interference and noise is solved. The article examines the issue of assessing the quality of this model, as well as meeting the re-quirements for ensuring a given value of the probability of correct classification of goals.

scholarly journals Cloud computing activities: Guidelines on the South African income tax classification

2017 ◽  
Vol 9 (1) ◽  
pp. 228-243
Author(s):  
Shené Steenkamp ◽  
Rudie Nel
Keyword(s):  
Cloud Computing ◽  
South African ◽  
Income Tax ◽  
Computer Hardware ◽  
Correct Classification ◽  
The South ◽  
Know How ◽  
Cloud Computing Service ◽  
Source Test

The classification of income from cloud computing activities, according to the substance-over-form doctrine, is fundamental to the application of the correct taxation source test. The designation of IaaS, PaaS and SaaS, the three main cloud computing service models, clearly denotes the form of cloud computing activities as that of a service. However, the nature of cloud computing inherently raises the question of whether or not cloud computing income should not rather be classified as income from leasing activities or the imparting of know-how. In fact, the findings of this study suggest the classification would not necessarily always be that of a service. The possible classification as lease income can be either income from the lease of tangible computer hardware and/or of intellectual property (royalty income). The aim of this study was to formulate guidelines to assist in the correct classification of income from cloud computing activities. This was achieved by performing doctrinal research based on the South African and international literature.

scholarly journals Impact of Feature Selection on Clustering Images ofVertebral Compression Fractures

2020 ◽  
Author(s):  
Raquel Candido ◽  
Rafael Lama ◽  
Natália Chiari ◽  
Marcello Nogueira-Barbosa ◽  
Paulo Azevedo Marques ◽  
...  
Keyword(s):  
Feature Selection ◽  
Metastatic Cancer ◽  
Vertebral Compression Fractures ◽  
Features Selection ◽  
Correct Classification ◽  
Compression Fractures ◽  
Vertebral Bodies ◽  
The Impact ◽  
Aided Diagnosis

Non-traumatic Vertebral Compression Fractures (VCFs) are generally caused by osteoporosis (benign VCFs) or metastatic cancer (malignant VCFs) and the success of the medical treatment strongly depends on a fast and correct classification of VCFs. Recently, methods for computer-aided diagnosis (CAD) based on machine learning have been proposed for classifying VCFs. In this work, we investigate the problem of clustering images of VCFs and the impact of feature selection by genetic algorithms, comparing the clustering i)with all features and ii)with feature selection through the purity results. The analysis of the clusters helps to understand the results of classifiers and difficulties of differentiating images of different classes by an expert. The results indicate that features selection improved the separability of clusters and purity. Feature selection also helps to understand which attributes are most important for analysing the images of vertebral bodies.

scholarly journals Identification of European and Japanese Larch and Their Interspecific Hybrid with Morphological Markers: Application to Young Seedlings

2006 ◽  
Vol 55 (1-6) ◽  
pp. 123-134 ◽  
Author(s):  
L. E. Pâques ◽  
G. Philippe ◽  
D. Prat
Keyword(s):  
Molecular Markers ◽  
Parental Species ◽  
Correct Classification ◽  
Morphological Markers ◽  
Hybrid Features ◽  
Pure Species ◽  
Seed Orchards ◽  
Linear Discriminant ◽  
Non Destructive

Abstract Open-pollinated hybridisation seed orchards of European and Japanese larches produce mixed progenies combining a highly variable proportion of hybrids along with pure parental species. For several reasons, it is desirable to identify and to sort out hybrids from pure species at the seedling stage. Taxa identification of 1-2 yr-old seedlings was attempted using non-destructive assessment of several traits, including morphology, phenology, growth and architecture parameters. Two sets of progenies originating from 10 open-pollinated hybridisation seed orchards were used, relying in a first step on taxa identification of individual seedlings with diagnostic molecular markers. Based on 21 traits assessed, some clear trends in pure species and hybrid features were apparent but due to the large and overlapping ranges of taxa characteristics, no single parameter allowed unambiguous identification of taxa. Combination of traits through linear discriminant analysis made possible correct classification of 90.2% to 98.6% of individuals depending on the orchard although there were a few problematic orchards. Two traits appeared particularly pertinent for discriminating young plants taxa, namely 1st-yr leaf retention (marcescence) and the bark colour of 2nd-year shoot increments. Results were corroborated using progenies from several orchards and over two experimental periods.

scholarly journals A Classification of Baire Class 1 Functions

1990 ◽  
Vol 318 (1) ◽  
pp. 209 ◽  
Author(s):  
A. S. Kechris ◽  
A. Louveau
Keyword(s):  
Baire Class ◽  
Class 1
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