Evaluation of radiography as a screening method for detection and characterisation of congenital vertebral malformations in dogs

Congenital vertebral malformations (CVM) are common in brachycephalic ‘screw-tailed’ dogs; they can be associated with neurological deficits and a genetic predisposition has been suggested. The purpose of this study was to evaluate radiography as a screening method for congenital thoracic vertebral malformations in brachycephalic ‘screw-tailed’ dogs by comparing it with CT. Forty-nine dogs that had both radiographic and CT evaluations of the thoracic vertebral column were included. Three observers retrospectively reviewed the images independently to detect CVMs. When identified, they were classified according to a previously published radiographic classification scheme. A CT consensus was then reached. All observers identified significantly more affected vertebrae when evaluating orthogonal radiographic views compared with lateral views alone; and more affected vertebrae with the CT consensus compared with orthogonal radiographic views. Given the high number of CVMs per dog, the number of dogs classified as being CVM free was not significantly different between CT and radiography. Significantly more midline closure defects were also identified with CT compared with radiography. Malformations classified as symmetrical or ventral hypoplasias on radiography were frequently classified as ventral and medial aplasias on CT images. Our results support that CT is better than radiography for the classification of CVMs and this will be important when further evidence of which are the most clinically relevant CVMs is identified. These findings are of particular importance for designing screening schemes of CVMs that could help selective breeding programmes based on phenotype and future studies.

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Everyday Pain in Three- to Five-Year-Old Children in Day Care

Pain Research and Management ◽

10.1155/1998/198043 ◽

1998 ◽

Vol 3 (2) ◽

pp. 111-116 ◽

Cited By ~ 18

Author(s):

Carl L von Baeyer ◽

Shannon Baskerville ◽

Patrick J McGrath

Keyword(s):

Day Care ◽

Classification Scheme ◽

Body Part ◽

First Aid ◽

Pain Scale ◽

Care Staff ◽

Future Studies ◽

Day Care Centres ◽

Median Rate

A new event sampling instrument, the Dalhousie Everyday Pain Scale, was used to observe 50 children in six day care centres in Saskatoon for an average of 2.24 h each. The nature of minor painful incidents (eg, collisions and falls) was recorded, including distress behaviours and responses from peers and adults. Twenty-nine children (58%) were observed to experience one or more painful incidents, producing a total of 51 incidents and yielding a median rate of incidents of 0.31 per child per hour, a rate similar to that reported in another Canadian sample. Seven of nine child response items met criteria for reliability in a subsample of incidents observed simultaneously by two observers. Rubbing the affected body part, crying and making verbal statements about the injury were the most common responses to painful incidents. Intervention by day care staff was strongly associated with children's facial expression of distress: physical and first aid interventions were offered most frequently to children who displayed the greatest facial distress. Content analysis of observers' records produced a classification scheme for causes of painful incidents. Twenty per cent of painful incidents were judged to be the result of deliberate actions by other children. The classification of causes may be a useful addition to the scale for application in future studies of everyday pain and injury prevention.

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Classification of Rainfall Types Using Parsivel Disdrometer and S-Band Polarimetric Radar in Central Korea

Remote Sensing ◽

10.3390/rs12040642 ◽

2020 ◽

Vol 12 (4) ◽

pp. 642 ◽

Cited By ~ 1

Author(s):

Jui Le Loh ◽

Dong-In Lee ◽

Mi-Young Kang ◽

Cheol-Hwan You

Keyword(s):

Vertical Profile ◽

Drop Size ◽

Classification Scheme ◽

Radar Data ◽

Number Concentration ◽

Polarimetric Radar ◽

Retrieval Method ◽

Median Volume ◽

Better Than

Tools to identify and classify stratiform and convective rains at various times of the 12 days from June 2015 to March 2016 in Jincheon, Korea, were developed by using a Parsivel disdrometer and S-band polarimetric (S-POL) radar data. Stratiform and convective rains were identified using three different methods (vertical profile of reflectivity (VPR), the method proposed by Bringi et al. (BR03), and a combination of the two (BR03-VPR)) by using a Parsivel disdrometer for its applications to radar as a reference. BR03-VPR exhibits a better classification scheme than the VPR and BR03 methods. The rain types were compared using the drop size distribution (DSD) retrieved from polarimetric variables and reflectivity only. By using the DSD variables, a new convective/stratiform classification line of the log-normalized droplet number concentration ( log 10 N w ) − median volume diameter ( D 0 ) was derived for this area to classify the rainfall types using DSD variables retrieved from the polarimetric radar. For the radar variables, the method by Steiner et al. (SHY95) was found to be the best method, with 0.00% misclassification of the stratiform rains. For the convective rains, the DSD retrieval method performed better. However, for both stratiform and convective rains, the fuzzy method performed better than the SHY95 and DSD retrieval methods.

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Computerized Diagnosis of Respira tory Disorders

Methods of Information in Medicine ◽

10.3414/me13-02-0041 ◽

2014 ◽

Vol 53 (04) ◽

pp. 291-295 ◽

Cited By ~ 5

Author(s):

M. Saraclar ◽

Y. P. Kahya ◽

I. Sen

Keyword(s):

Pulmonary Disease ◽

Classification Scheme ◽

Support Vector ◽

Respiratory Sounds ◽

Svm Classifier ◽

Model Parameters ◽

Vector Autoregressive ◽

Future Studies ◽

Interstitial Pulmonary Disease

SummaryIntroduction: This article is part of the Focus Theme of Methods of Information in Medicine on “Biosignal Interpretation: Advanced Methods for Studying Cardiovascular and Respiratory Systems”.Objectives: This work proposes an algorithm for diagnostic classification of multi-channel respiratory sounds.Methods: 14-channel respiratory sounds are modeled assuming a 250-point second order vector autoregressive (VAR) process, and the estimated model parameters are used to feed a support vector machine (SVM) classifier. Both a three-class classifier (healthy, bronchi ectasis and interstitial pulmonary disease) and a binary classifier (healthy versus pathological) are considered.Results: In the binary scheme, the sensitivity and specificity for both classes are 85% ± 8.2%. In the three-class classification scheme, the healthy recall (95% ± 5%) and the interstitial pulmonary disease recall and precision (100% ± 0% both) are rather high. However, bronchiectasis recall is very low (30% ± 15.3%), resulting in poor healthy and bronchiectasis precision rates (76% ± 8.7% and 75% ± 25%, respectively). The main reason behind these poor rates is that the bronchiectasis is confused with the healthy case.Conclusions: The proposed method is promising, nevertheless, it should be improved such that other mathematical models, additional features, and/or other classifiers are to be experimented in future studies.

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Values in Action Inventory of Strengths (VIA-IS): Translation and Validation in Urdu Language

Pakistan Journal of Psychological Research ◽

10.33824/pjpr.2020.35.1.10 ◽

2020 ◽

Vol 35 (1) ◽

pp. 163-189

Author(s):

Afifa Anjum ◽

Naumana Amjad

Keyword(s):

Factor Structure ◽

Convergent Validity ◽

Character Strengths ◽

Future Studies ◽

Structure Factors ◽

Adult Participants ◽

Positive Correlations ◽

Cross Language ◽

Translation And Validation

Values in Action is a classification of 24 character strengths grouped under six virtue categories. This classification is claimed to be universal across cultures and religions (Peterson & Seligman, 2004) and its measure that is, Values in Action Inventory of Strengths (VIA-IS) has been translated and validated in many languages. The present study aimed at its Urdu translation and validation on Pakistani adults taken from different educational institutes and workplaces. Study comprised two parts. Part I dealt with the translation and cross-language validation while in Part II, Construct validation on a sample of 542 adults and convergent validity on a sample of 210 adult participants were determined. Findings revealed satisfactory alpha coefficients for Urdu version. Significant positive correlations with positive affect and life satisfaction and negative correlations with negative affect were indicators of its convergent validity. Age was negatively associated with five strengths whereas significant gender differences were found on seven strengths. Social desirability effects were nonsignificant. Strength-to-virtue level factor structure exploration resulted in a theoretically meaningful four factor structure. Factors were named as Interpersonal, Cognitive, Vitality, and Transcendence and were comparable to factor structures proposed in studies on VIA-IS from a few other cultures. The study offers a valid Urdu translation for use in future studies with adult Urdu speaking population.

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Some of Frontiers Education Articles: Around First to Eleven December 2020

10.35542/osf.io/8964e ◽

2020 ◽

Author(s):

thobias sarbunan

Keyword(s):

Knowledge Creation ◽

Publishing House ◽

General Point ◽

Human Race ◽

Future Studies ◽

Long Period ◽

Research And Innovation ◽

New Information ◽

Scientific Ideas ◽

Better Than

The research pathway is also an important point to lead researchers in creating and enriching knowledge from a fresh viewpoint, as wellas development for the human race. The frontier is the publishing house of a publication that has established information along with the'other agent' of knowledge around the globe. As a result, one of the sub-journals of this publication was education, expanded awarenesstime by time, by new information on innovation in science and technology. In the meantime, the pandemic, better than the science society,has alerted to the current developments in science aimed at strengthening and gaining some insight and awareness of how to maintainthe 'mode of knowledge creation'. So, through this discussion of the current edition of Frontier Education Journals, I thought that thisdiscussion theoretically involved encouragement and advancement in the middle of the pandemic, also influenced from a general point ofview, here as roadmap or step-stone for all research and innovation researchers. On the basis of the discussion in general, I saw that theroad map of the topic of frontier education is in significance to all branches of expertise of education. I agree that knowledge developmenttime-by-time needs to be reflected-analysed-synthesized-adopted or adapted-also developed for the purpose of education in addition tolearning from a general viewpoint. Note, knowledge is never-never sleeping tight, but it still evolves and progresses a long period with thenewest scientific ideas-concept-and hypothesis. In the other hand, it is possible that my study would miss a range of weaknesses inliteracy resources as well; but at least, I have sought, through this article, to see the importance of knowledge advancement that can enrichknowledge in the middle of the pandemic and for future studies.

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A Machine Learning Approach to Study Glycosidase Activities from Bifidobacterium

Microorganisms ◽

10.3390/microorganisms9051034 ◽

2021 ◽

Vol 9 (5) ◽

pp. 1034

Author(s):

Carlos Sabater ◽

Lorena Ruiz ◽

Abelardo Margolles

Keyword(s):

Machine Learning ◽

Supervised Classification ◽

Machine Learning Algorithms ◽

Learning Approach ◽

Human Milk Oligosaccharides ◽

Future Studies ◽

High Fiber ◽

Machine Learning Approach ◽

Prebiotic Oligosaccharides

This study aimed to recover metagenome-assembled genomes (MAGs) from human fecal samples to characterize the glycosidase profiles of Bifidobacterium species exposed to different prebiotic oligosaccharides (galacto-oligosaccharides, fructo-oligosaccharides and human milk oligosaccharides, HMOs) as well as high-fiber diets. A total of 1806 MAGs were recovered from 487 infant and adult metagenomes. Unsupervised and supervised classification of glycosidases codified in MAGs using machine-learning algorithms allowed establishing characteristic hydrolytic profiles for B. adolescentis, B. bifidum, B. breve, B. longum and B. pseudocatenulatum, yielding classification rates above 90%. Glycosidase families GH5 44, GH32, and GH110 were characteristic of B. bifidum. The presence or absence of GH1, GH2, GH5 and GH20 was characteristic of B. adolescentis, B. breve and B. pseudocatenulatum, while families GH1 and GH30 were relevant in MAGs from B. longum. These characteristic profiles allowed discriminating bifidobacteria regardless of prebiotic exposure. Correlation analysis of glycosidase activities suggests strong associations between glycosidase families comprising HMOs-degrading enzymes, which are often found in MAGs from the same species. Mathematical models here proposed may contribute to a better understanding of the carbohydrate metabolism of some common bifidobacteria species and could be extrapolated to other microorganisms of interest in future studies.

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NLOS Multipath Classification of GNSS Signal Correlation Output Using Machine Learning

Sensors ◽

10.3390/s21072503 ◽

2021 ◽

Vol 21 (7) ◽

pp. 2503

Author(s):

Taro Suzuki ◽

Yoshiharu Amano

Keyword(s):

Machine Learning ◽

Satellite System ◽

Training Data ◽

Support Vector ◽

Positioning Errors ◽

Automated Method ◽

Global Navigation Satellite ◽

Better Than ◽

Signal Correlation

This paper proposes a method for detecting non-line-of-sight (NLOS) multipath, which causes large positioning errors in a global navigation satellite system (GNSS). We use GNSS signal correlation output, which is the most primitive GNSS signal processing output, to detect NLOS multipath based on machine learning. The shape of the multi-correlator outputs is distorted due to the NLOS multipath. The features of the shape of the multi-correlator are used to discriminate the NLOS multipath. We implement two supervised learning methods, a support vector machine (SVM) and a neural network (NN), and compare their performance. In addition, we also propose an automated method of collecting training data for LOS and NLOS signals of machine learning. The evaluation of the proposed NLOS detection method in an urban environment confirmed that NN was better than SVM, and 97.7% of NLOS signals were correctly discriminated.

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Deep Learning Methods for Classification of Certain Abnormalities in Echocardiography

Electronics ◽

10.3390/electronics10040495 ◽

2021 ◽

Vol 10 (4) ◽

pp. 495

Author(s):

Imayanmosha Wahlang ◽

Arnab Kumar Maji ◽

Goutam Saha ◽

Prasun Chakrabarti ◽

Michal Jasinski ◽

...

Keyword(s):

Deep Learning ◽

Short Term Memory ◽

Support Vector ◽

Variational Autoencoder ◽

Different Types ◽

Static Images ◽

Long Short Term Memory ◽

2D And 3D ◽

Better Than

This article experiments with deep learning methodologies in echocardiogram (echo), a promising and vigorously researched technique in the preponderance field. This paper involves two different kinds of classification in the echo. Firstly, classification into normal (absence of abnormalities) or abnormal (presence of abnormalities) has been done, using 2D echo images, 3D Doppler images, and videographic images. Secondly, based on different types of regurgitation, namely, Mitral Regurgitation (MR), Aortic Regurgitation (AR), Tricuspid Regurgitation (TR), and a combination of the three types of regurgitation are classified using videographic echo images. Two deep-learning methodologies are used for these purposes, a Recurrent Neural Network (RNN) based methodology (Long Short Term Memory (LSTM)) and an Autoencoder based methodology (Variational AutoEncoder (VAE)). The use of videographic images distinguished this work from the existing work using SVM (Support Vector Machine) and also application of deep-learning methodologies is the first of many in this particular field. It was found that deep-learning methodologies perform better than SVM methodology in normal or abnormal classification. Overall, VAE performs better in 2D and 3D Doppler images (static images) while LSTM performs better in the case of videographic images.

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