scholarly journals Description and classification of bivalve mollusks hemocytes: a computational approach

2018 ◽  
Author(s):  
Yuri A Karetin ◽  
Aleksandra A Kalitnik ◽  
Alina E Safonova ◽  
Eduardas Cicinskas
Keyword(s):  
Classification Scheme ◽  
Euclidean Geometry ◽  
Fractal Dimensions ◽  
Classification Model ◽  
Bivalve Mollusks ◽  
Optimal Model ◽  
Fractal Parameters ◽  
Spisula Sachalinensis

The fractal formalism in combination with linear image analysis enables statistically significant description and classification of “irregular” (in terms of Euclidean geometry) shapes, such as, outlines of in vitro flattened cells. We developed an optimal model for classifying bivalve Spisula sachalinensis and Callista brevisiphonata immune cells, based on evaluating their linear and non-linear morphological features: dimensional characteristics (area, perimeter), various parameters of cell bounding circle, convex hull, cell symmetry, roundness, and a number of fractal dimensions and lacunarities evaluating the spatial complexity of cells. Proposed classification model is based on Ward’s clustering method, loaded with highest multimodality index factors. This classification scheme groups cells into three morphological types, which can be distinguished both visually and by several linear and quasi-fractal parameters.

scholarly journals Description and classification of bivalve mollusks hemocytes: a computational approach

2018 ◽  
Author(s):  
Yuri A Karetin ◽  
Aleksandra A Kalitnik ◽  
Alina E Safonova ◽  
Eduardas Cicinskas
Keyword(s):  
Classification Scheme ◽  
Euclidean Geometry ◽  
Fractal Dimensions ◽  
Classification Model ◽  
Bivalve Mollusks ◽  
Optimal Model ◽  
Fractal Parameters ◽  
Spisula Sachalinensis

The fractal formalism in combination with linear image analysis enables statistically significant description and classification of “irregular” (in terms of Euclidean geometry) shapes, such as, outlines of in vitro flattened cells. We developed an optimal model for classifying bivalve Spisula sachalinensis and Callista brevisiphonata immune cells, based on evaluating their linear and non-linear morphological features: dimensional characteristics (area, perimeter), various parameters of cell bounding circle, convex hull, cell symmetry, roundness, and a number of fractal dimensions and lacunarities evaluating the spatial complexity of cells. Proposed classification model is based on Ward’s clustering method, loaded with highest multimodality index factors. This classification scheme groups cells into three morphological types, which can be distinguished both visually and by several linear and quasi-fractal parameters.

scholarly journals Description and classification of bivalve mollusks hemocytes: a computational approach

PeerJ ◽  
2019 ◽  
Vol 7 ◽  
pp. e7056
Author(s):  
Yuriy A. Karetin ◽  
Aleksandra A. Kalitnik ◽  
Alina E. Safonova ◽  
Eduardas Cicinskas
Keyword(s):  
Classification Scheme ◽  
Euclidean Geometry ◽  
Fractal Dimensions ◽  
Classification Model ◽  
Optimal Model ◽  
Fractal Parameters ◽  
Callista Brevisiphonata ◽  
Spisula Sachalinensis

The fractal formalism in combination with linear image analysis enables statistically significant description and classification of “irregular” (in terms of Euclidean geometry) shapes, such as, outlines ofin vitroflattened cells. We developed an optimal model for classifying bivalveSpisula sachalinensisandCallista brevisiphonataimmune cells, based on evaluating their linear and non-linear morphological features: size characteristics (area, perimeter), various parameters of cell bounding circle, convex hull, cell symmetry, roundness, and a number of fractal dimensions and lacunarities evaluating the spatial complexity of cells. Proposed classification model is based on Ward’s clustering method, loaded with highest multimodality index factors. This classification scheme groups cells into three morphological types, which can be distinguished both visually and by several linear and quasi-fractal parameters.

scholarly journals Breakpoints for the Classification of Anti-Candida Compounds in Antifungal Screening

2021 ◽  
Vol 2021 ◽  
pp. 1-8
Author(s):  
Danielle da Nóbrega Alves ◽  
Alana Rodrigues Ferreira ◽  
Allana Brunna Sucupira Duarte ◽  
Ana Karoline Vieira Melo ◽  
Damião Pergentino de Sousa ◽  
...  
Keyword(s):  
Candida Species ◽  
Classification Scheme ◽  
New Drugs ◽  
In Vitro Tests ◽  
Exclusion Criteria ◽  
Drug Candidates ◽  
Antifungal Potential ◽  
Reference Strains

Introduction. The absence of a standardized classification scheme for the antifungal potency of compounds screened against Candida species may hinder the study of new drugs. This systematic review proposes a scheme of interpretative breakpoints for the minimum inhibitory concentration (MIC) of bioactive compounds against Candida species in in vitro tests. Materials and Methods. A literature search was conducted in the PubMed, Scopus, Web of Science, Lilacs, and SciFinder databases for the period from January 2015 to April 2020. The following inclusion criterion was used: organic compounds tested by the microdilution technique according to the Clinical and Laboratory Standards Institute protocol against reference strains of the genus Candida. A total of 545 articles were retrieved after removing duplicates. Of these, 106 articles were selected after applying the exclusion criteria and were evaluated according to the number of synthesized molecules and their chemical classes, the type of strain (reference or clinical) used in the antifungal test, the Candida species, and the MIC (in μg/mL) used. Results. The analysis was performed based on the median, quartiles (25% and 75%), maximum, and minimum values of four groups: all strains, ATCC strains, C. albicans strains, and C. albicans ATCC strains. The following breakpoints were proposed to define the categories: MIC < 3.515   μ g / mL (very strong bioactivity); 3.516-25 μg/mL (strong bioactivity); 26-100 μg/mL (moderate bioactivity); 101-500 μg/mL (weak bioactivity); 500-2000 μg/mL (very weak bioactivity); and >2000 μg/mL (no bioactivity). Conclusions. A classification scheme of the antifungal potency of compounds against Candida species is proposed that can be used to identify the antifungal potential of new drug candidates.

Connectionist Temporal Classification Model for Dynamic Hand Gesture Recognition using RGB and Optical flow Data

2020 ◽  
Vol 17 (4) ◽  
pp. 497-506
Author(s):  
Sunil Patel ◽  
Ramji Makwana
Keyword(s):  
Neural Network ◽  
Optical Flow ◽  
Gesture Recognition ◽  
Hand Gesture Recognition ◽  
Classification Model ◽  
Hand Gesture ◽  
Flow Data ◽  
Dynamic Hand Gesture Recognition ◽  
Connectionist Temporal Classification

Automatic classification of dynamic hand gesture is challenging due to the large diversity in a different class of gesture, Low resolution, and it is performed by finger. Due to a number of challenges many researchers focus on this area. Recently deep neural network can be used for implicit feature extraction and Soft Max layer is used for classification. In this paper, we propose a method based on a two-dimensional convolutional neural network that performs detection and classification of hand gesture simultaneously from multimodal Red, Green, Blue, Depth (RGBD) and Optical flow Data and passes this feature to Long-Short Term Memory (LSTM) recurrent network for frame-to-frame probability generation with Connectionist Temporal Classification (CTC) network for loss calculation. We have calculated an optical flow from Red, Green, Blue (RGB) data for getting proper motion information present in the video. CTC model is used to efficiently evaluate all possible alignment of hand gesture via dynamic programming and check consistency via frame-to-frame for the visual similarity of hand gesture in the unsegmented input stream. CTC network finds the most probable sequence of a frame for a class of gesture. The frame with the highest probability value is selected from the CTC network by max decoding. This entire CTC network is trained end-to-end with calculating CTC loss for recognition of the gesture. We have used challenging Vision for Intelligent Vehicles and Applications (VIVA) dataset for dynamic hand gesture recognition captured with RGB and Depth data. On this VIVA dataset, our proposed hand gesture recognition technique outperforms competing state-of-the-art algorithms and gets an accuracy of 86%

scholarly journals Growth Stages Classification of Potato Crop Based on Analysis of Spectral Response and Variables Optimization

Sensors ◽  
2020 ◽  
Vol 20 (14) ◽  
pp. 3995 ◽  
Author(s):  
Ning Liu ◽  
Ruomei Zhao ◽  
Lang Qiao ◽  
Yao Zhang ◽  
Minzan Li ◽  
...  
Keyword(s):  
Growth Stage ◽  
Spectral Response ◽  
Classification Model ◽  
Growth Stages ◽  
Potato Field ◽  
Spectral Bands ◽  
Svm Model ◽  
Selection Algorithms ◽  
Potato Crops

Potato is the world’s fourth-largest food crop, following rice, wheat, and maize. Unlike other crops, it is a typical root crop with a special growth cycle pattern and underground tubers, which makes it harder to track the progress of potatoes and to provide automated crop management. The classification of growth stages has great significance for right time management in the potato field. This paper aims to study how to classify the growth stage of potato crops accurately on the basis of spectroscopy technology. To develop a classification model that monitors the growth stage of potato crops, the field experiments were conducted at the tillering stage (S1), tuber formation stage (S2), tuber bulking stage (S3), and tuber maturation stage (S4), respectively. After spectral data pre-processing, the dynamic changes in chlorophyll content and spectral response during growth were analyzed. A classification model was then established using the support vector machine (SVM) algorithm based on spectral bands and the wavelet coefficients obtained from the continuous wavelet transform (CWT) of reflectance spectra. The spectral variables, which include sensitive spectral bands and feature wavelet coefficients, were optimized using three selection algorithms to improve the classification performance of the model. The selection algorithms include correlation analysis (CA), the successive projection algorithm (SPA), and the random frog (RF) algorithm. The model results were used to compare the performance of various methods. The CWT-SPA-SVM model exhibited excellent performance. The classification accuracies on the training set (Atrain) and the test set (Atest) were respectively 100% and 97.37%, demonstrating the good classification capability of the model. The difference between the Atrain and accuracy of cross-validation (Acv) was 1%, which showed that the model has good stability. Therefore, the CWT-SPA-SVM model can be used to classify the growth stages of potato crops accurately. This study provides an important support method for the classification of growth stages in the potato field.

scholarly journals CirBiTree: Citrullination Site Inference Based on a Fuzzy Neural Network and Flexible Neural Tree

2020 ◽  
Vol 2020 ◽  
pp. 1-8
Author(s):  
Chuandong Song ◽  
Haifeng Wang
Keyword(s):  
Neural Network ◽  
Fuzzy Neural Network ◽  
Classification Model ◽  
Peptide Sequence ◽  
Sequence Information ◽  
Post Translational Modification ◽  
Fuzzy Neural ◽  
Human Complex ◽  
Better Than

Emerging evidence demonstrates that post-translational modification plays an important role in several human complex diseases. Nevertheless, considering the inherent high cost and time consumption of classical and typical in vitro experiments, an increasing attention has been paid to the development of efficient and available computational tools to identify the potential modification sites in the level of protein. In this work, we propose a machine learning-based model called CirBiTree for identification the potential citrullination sites. More specifically, we initially utilize the biprofile Bayesian to extract peptide sequence information. Then, a flexible neural tree and fuzzy neural network are employed as the classification model. Finally, the most available length of identified peptides has been selected in this model. To evaluate the performance of the proposed methods, some state-of-the-art methods have been employed for comparison. The experimental results demonstrate that the proposed method is better than other methods. CirBiTree can achieve 83.07% in sn%, 80.50% in sp, 0.8201 in F1, and 0.6359 in MCC, respectively.

In Vitro Toxicology Methods in Risk Assessment

1996 ◽  
Vol 24 (3) ◽  
pp. 325-331
Author(s):  
Iain F. H. Purchase
Keyword(s):  
Risk Assessment ◽  
Hazard Assessment ◽  
Human Health Risk ◽  
In Vitro Test ◽  
In Vitro Methods ◽  
New Concepts ◽  
Vitro Test

The title of this paper is challenging, because the question of how in vitro methods and results contribute to human health risk assessment is rarely considered. The process of risk assessment usually begins with hazard assessment, which provides a description of the inherent toxicological properties of the chemical. The next step is to assess the relevance of this to humans, i.e. the human hazard assessment. Finally, information on exposure is examined, and risk can then be assessed. In vitro methods have a limited, but important, role to play in risk assessment. The results can be used for classification and labelling; these are methods of controlling exposure, analogous to risk assessment, but without considering exposure. The Ames Salmonella test is the only in vitro method which is incorporated into regulations and used widely. Data from this test can, at best, lead to classification of a chemical with regard to genotoxicity, but cannot be used for classification and labelling on their own. Several in vitro test systems which assess the topical irritancy and corrosivity of chemicals have been reasonably well validated, and the results from these tests can be used for classification. The future development of in vitro methods is likely to be slow, as it depends on the development of new concepts and ideas. The in vivo methods which currently have reasonably developed in vitro alternatives will be the easiest to replace. The remaining in vivo methods, which provide toxicological information from repeated chronic dosing, with varied endpoints and by mechanisms which are not understood, will be more difficult to replace.

scholarly journals Uncertainty-Aware Deep Learning-Based Cardiac Arrhythmias Classification Model of Electrocardiogram Signals

Computers ◽  
2021 ◽  
Vol 10 (6) ◽  
pp. 82
Author(s):  
Ahmad O. Aseeri
Keyword(s):  
Deep Learning ◽  
Cardiac Arrhythmias ◽  
Large Scale ◽  
Clinical Decision Making ◽  
Probabilistic Approach ◽  
Classification Model ◽  
Gating Mechanism ◽  
Uncertainty Estimates ◽  
Wide Range

Deep Learning-based methods have emerged to be one of the most effective and practical solutions in a wide range of medical problems, including the diagnosis of cardiac arrhythmias. A critical step to a precocious diagnosis in many heart dysfunctions diseases starts with the accurate detection and classification of cardiac arrhythmias, which can be achieved via electrocardiograms (ECGs). Motivated by the desire to enhance conventional clinical methods in diagnosing cardiac arrhythmias, we introduce an uncertainty-aware deep learning-based predictive model design for accurate large-scale classification of cardiac arrhythmias successfully trained and evaluated using three benchmark medical datasets. In addition, considering that the quantification of uncertainty estimates is vital for clinical decision-making, our method incorporates a probabilistic approach to capture the model’s uncertainty using a Bayesian-based approximation method without introducing additional parameters or significant changes to the network’s architecture. Although many arrhythmias classification solutions with various ECG feature engineering techniques have been reported in the literature, the introduced AI-based probabilistic-enabled method in this paper outperforms the results of existing methods in outstanding multiclass classification results that manifest F1 scores of 98.62% and 96.73% with (MIT-BIH) dataset of 20 annotations, and 99.23% and 96.94% with (INCART) dataset of eight annotations, and 97.25% and 96.73% with (BIDMC) dataset of six annotations, for the deep ensemble and probabilistic mode, respectively. We demonstrate our method’s high-performing and statistical reliability results in numerical experiments on the language modeling using the gating mechanism of Recurrent Neural Networks.

scholarly journals CNN-Based Acoustic Scene Classification System

Electronics ◽  
2021 ◽  
Vol 10 (4) ◽  
pp. 371
Author(s):  
Yerin Lee ◽  
Soyoung Lim ◽  
Il-Youp Kwak
Keyword(s):  
Low Complexity ◽  
Classification Model ◽  
Weight Class ◽  
Scene Classification ◽  
Audio File ◽  
General Classification ◽  
Team Task ◽  
Real World Applications ◽  
Activation Mapping

Acoustic scene classification (ASC) categorizes an audio file based on the environment in which it has been recorded. This has long been studied in the detection and classification of acoustic scenes and events (DCASE). This presents the solution to Task 1 of the DCASE 2020 challenge submitted by the Chung-Ang University team. Task 1 addressed two challenges that ASC faces in real-world applications. One is that the audio recorded using different recording devices should be classified in general, and the other is that the model used should have low-complexity. We proposed two models to overcome the aforementioned problems. First, a more general classification model was proposed by combining the harmonic-percussive source separation (HPSS) and deltas-deltadeltas features with four different models. Second, using the same feature, depthwise separable convolution was applied to the Convolutional layer to develop a low-complexity model. Moreover, using gradient-weight class activation mapping (Grad-CAM), we investigated what part of the feature our model sees and identifies. Our proposed system ranked 9th and 7th in the competition for these two subtasks, respectively.

scholarly journals Sensitive protein misfolding cyclic amplification of sporadic Creutzfeldt–Jakob disease prions is strongly seed and substrate dependent

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Maxime Bélondrade ◽  
Simon Nicot ◽  
Charly Mayran ◽  
Lilian Bruyere-Ostells ◽  
Florian Almela ◽  
...  
Keyword(s):  
Prion Protein ◽  
Protein Misfolding ◽  
Protein Misfolding Cyclic Amplification ◽  
Bank Voles ◽  
Substrate Dependence ◽  
Brain Extracts ◽  
Jakob Disease ◽  
Human Prion Protein

AbstractUnlike variant Creutzfeldt–Jakob disease prions, sporadic Creutzfeldt–Jakob disease prions have been shown to be difficult to amplify in vitro by protein misfolding cyclic amplification (PMCA). We assessed PMCA of pathological prion protein (PrPTSE) from 14 human sCJD brain samples in 3 substrates: 2 from transgenic mice expressing human prion protein (PrP) with either methionine (M) or valine (V) at position 129, and 1 from bank voles. Brain extracts representing the 5 major clinicopathological sCJD subtypes (MM1/MV1, MM2, MV2, VV1, and VV2) all triggered seeded PrPTSE amplification during serial PMCA with strong seed- and substrate-dependence. Remarkably, bank vole PrP substrate allowed the propagation of all sCJD subtypes with preservation of the initial molecular PrPTSE type. In contrast, PMCA in human PrP substrates was accompanied by a PrPTSE molecular shift during heterologous (M/V129) PMCA reactions, with increased permissiveness of V129 PrP substrate to in vitro sCJD prion amplification compared to M129 PrP substrate. Combining PMCA amplification sensitivities with PrPTSE electrophoretic profiles obtained in the different substrates confirmed the classification of 4 distinct major sCJD prion strains (M1, M2, V1, and V2). Finally, the level of sensitivity required to detect VV2 sCJD prions in cerebrospinal fluid was achieved.

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