Use of T-RFLP and seven restriction enzymes to compare the faecal microbiota of obese and lean Japanese healthy men

2015 ◽  
Vol 6 (5) ◽  
pp. 735-745 ◽  
Author(s):  
T. Kobayashi ◽  
T. Osaki ◽  
S. Oikawa
Keyword(s):  
Data Mining ◽  
Pearson Correlation ◽  
Correlation Coefficients ◽  
Restriction Enzymes ◽  
Japanese Men ◽  
Operational Taxonomic Units ◽  
Length Polymorphisms ◽  
Bacillus Spp ◽  
Obese Subjects ◽  
Using Data

The composition of the intestinal microbiota of 92 healthy Japanese men was measured following consumption of identical meals for 3 days; terminal restriction fragment length polymorphisms were then used to analyse the DNA content of their faeces. The obtained operational taxonomic units (OTUs) were further analysed using seven restriction enzymes: 516f-BslI and -HaeIII, 27f-MspI and -AluI, and 35f-HhaI, -MspI and -AluI. Subjects were classified by their body mass index (BMI) as lean (<18.5) or obese (>25.0). OTUs were then analysed using data mining software. Pearson correlation coefficients on data mining results indicated only a weak relationship between BMI and OTU diversity. Specific OTUs attributed to lean and obese subjects were further examined by data mining with six groups of enzymes and closely related accession numbers for lean and obese subjects were successfully narrowed down. 16S rRNA sequences showed Bacillus spp., Erysipelothrix spp. and Holdemania spp. to be present among 30 bacterial candidates related to the lean group. Fifteen candidates were classified Firmicutes, one was classified as Chloroflexi, and the others were not classified. 45 Microbacteriaceae, 11 uncultured Actinobacterium, and 3 other families were present among the 119 candidate OTUs related to obesity. We conclude that the presence of Firmicutes and Actinobacteria may be related to the BMI of the subject.

Correlations Between Two Plaque Indices in Assessment of Toothbrush Effectiveness

2006 ◽  
Vol 7 (5) ◽  
pp. 1-9 ◽  
Author(s):  
MaryAnn Cugini ◽  
Maureen Thompson ◽  
Paul R. Warren
Keyword(s):  
Pearson Correlation ◽  
Correlation Coefficients ◽  
Plaque Score ◽  
Plaque Removal ◽  
Minimum Score ◽  
Single Use ◽  
Positive Correlations ◽  
Using Data ◽  
Clinical Measurements ◽  
Study Designs

Abstract Background The Rustogi et al. Modified Navy (RMNPI) and Turesky et al Modification of the Quigley Hein (TQHPI) plaque indices are commonly used to measure plaque removal. This study evaluated the possible correlations of both indices using data relative to a single use assessment of plaque removal using commercially available toothbrushes. Methods Single use crossover study designs have been previously reported. Disclosed plaque was scored pre- and post-brushing using both the RMNPI and the TQHPI. Sixty subjects, with an initial mean RMNPI score of 0.6 or greater, were enrolled and completed the study. No minimum score was required for TQHPI. After the initial scoring, the order for each index was randomized so that each subject was scored with either RMNPI followed by TQHPI or vice versa. Two manual toothbrushes [Oral-B® CrossAction® (CA) and Colgate® Navigator. (NA)] and one battery-powered brush (Crest® SpinBrush. Pro) (SBP) were evaluated in the trial. One examiner performed all clinical measurements. Pearson correlations were performed on whole mouth, buccal, and lingual plaque scores for the CA toothbrush. Results Strong positive correlations were found between the two plaque indices for pre- and post-brushing scores for the whole mouth and on lingual and buccal surfaces, where Pearson correlation coefficients ranged between 0.963 and 0.995. There was no correlation between the pre-brushing plaque score and the amount of plaque removed by brushing indicating that higher plaque levels before brushing do not necessarily predict that greater amounts of plaque will be removed during toothbrushing. Each toothbrush was found to be safe and significantly reduced plaque levels after a single brushing (t-test, p=0.0001). Significantly greater plaque reductions were found with the CA than the NA and SBP toothbrushes at whole mouth, lingual, and approximal surfaces for both indices (analysis of variance (ANOVA), p . 0.0002 for all comparisons). Conclusions Strong positive correlations were found between two plaque indices (the RMNPI and TQHPI) for pre- and post-brushing scores at whole mouth, lingual, and buccal surfaces as assessed using data from a single use assessment of plaque removal. Efficacy data from this study demonstrated the CA toothbrush provided superior cleaning when compared to the NA manual toothbrush and SBP battery toothbrush. Clinical Implications Two commonly used indices for assessing plaque removal in clinical studies are RMNPI and TQHPI. However, each index differs in the way plaque is scored. This study used both indices to assess comparative toothbrush efficacy and showed a strong correlation between indices for both pre- and postbrushing plaque scores. The result suggests that both indices demonstrate sufficient sensitivity to differentiate toothbrush efficacy. Citation Cugini M, Thompson M, Warren PR. Correlations Between Two Plaque Indices in Assessment of Toothbrush Effectiveness. J Contemp Dent Pract 2006 November;(7)5:001-009.

scholarly journals Kano Model Integration with Data Mining to Predict Customer Satisfaction

2021 ◽  
Vol 5 (4) ◽  
pp. 66
Author(s):  
Khaled Al Rabaiei ◽  
Fady Alnajjar ◽  
Amir Ahmad
Keyword(s):  
Data Mining ◽  
Customer Satisfaction ◽  
Significant Contribution ◽  
Pearson Correlation ◽  
Feature Selection Method ◽  
Correlation Coefficients ◽  
Selection Method ◽  
Features Selection ◽  
Questionnaire Data ◽  
Kano Model

The Kano model is one of the models that help determine which features must be included in a product or service to improve customer satisfaction. The model is focused on highlighting the most relevant attributes of a product or service along with customers’ estimation of how the presence of these attributes can be used to predict satisfaction about specific services or products. This research aims to develop a method to integrate the Kano model and data mining approaches to select relevant attributes that drive customer satisfaction, with a specific focus on higher education. The significant contribution of this research is to solve the problem of selecting features that are not methodically correlated to customer satisfaction, which could reduce the risk of investing in features that could ultimately be irrelevant to enhancing customer satisfaction. Questionnaire data were collected from 646 students from UAE University. The experiment suggests that XGBoost Regression and Decision Tree Regression produce best results for this kind of problem. Based on the integration between the Kano model and the feature selection method, the number of features used to predict customer satisfaction is minimized to four features. It was found that ANOVA features selection model’s integration with the Kano model gives higher Pearson correlation coefficients and higher R2 values.

Reliability of the Minimum Masking Level as Outcome Variable in Tinnitus Clinical Research

2020 ◽  
Vol 29 (3) ◽  
pp. 429-435
Author(s):  
Patricia C. Mancini ◽  
Richard S. Tyler ◽  
Hyung Jin Jun ◽  
Tang-Chuan Wang ◽  
Helena Ji ◽  
...  
Keyword(s):  
Standard Deviation ◽  
High Frequency ◽  
Pearson Correlation ◽  
Correlation Coefficients ◽  
Outcome Variable ◽  
Reliable Estimate ◽  
Moderate Correlation ◽  
Trained Subjects ◽  
The Relationship ◽  
Minimum Intensity

Purpose The minimum masking level (MML) is the minimum intensity of a stimulus required to just totally mask the tinnitus. Treatments aimed at reducing the tinnitus itself should attempt to measure the magnitude of the tinnitus. The objective of this study was to evaluate the reliability of the MML. Method Sample consisted of 59 tinnitus patients who reported stable tinnitus. We obtained MML measures on two visits, separated by about 2–3 weeks. We used two noise types: speech-shaped noise and high-frequency emphasis noise. We also investigated the relationship between the MML and tinnitus loudness estimates and the Tinnitus Handicap Questionnaire (THQ). Results There were differences across the different noise types. The within-session standard deviation averaged across subjects varied between 1.3 and 1.8 dB. Across the two sessions, the Pearson correlation coefficients, range was r = .84. There was a weak relationship between the dB SL MML and loudness, and between the MML and the THQ. A moderate correlation ( r = .44) was found between the THQ and loudness estimates. Conclusions We conclude that the dB SL MML can be a reliable estimate of tinnitus magnitude, with expected standard deviations in trained subjects of about 1.5 dB. It appears that the dB SL MML and loudness estimates are not closely related.

Analysis of Crop Yield Prediction of Kharif & Rabi Jowar Crops Using Data Mining Techniques

2017 ◽  
Vol 7 (11) ◽  
pp. 79
Author(s):  
Sujata Mulik
Keyword(s):  
Data Mining ◽  
Crop Yield ◽  
Crop Production ◽  
Climatic Factors ◽  
Crop Productivity ◽  
Yield Prediction ◽  
Data Mining Techniques ◽  
Agriculture Sector ◽  
Using Data ◽  
Rabi Crops

Agriculture sector in India is facing rigorous problem to maximize crop productivity. More than 60 percent of the crop still depends on climatic factors like rainfall, temperature, humidity. This paper discusses the use of various Data Mining applications in agriculture sector. Data Mining is used to solve various problems in agriculture sector. It can be used it to solve yield prediction.  The problem of yield prediction is a major problem that remains to be solved based on available data. Data mining techniques are the better choices for this purpose. Different Data Mining techniques are used and evaluated in agriculture for estimating the future year's crop production. In this paper we have focused on predicting crop yield productivity of kharif & Rabi Crops. 

Perancangan Aplikasi Prediksi Kelulusan Tepat Waktu Bagi Mahasiswa Baru Dengan Teknik Data Mining (Studi Kasus: Data Akademik Mahasiswa STMIK Dipanegara Makassar)

2015 ◽  
Vol 1 (4) ◽  
pp. 270
Author(s):  
Muhammad Syukri Mustafa ◽  
I. Wayan Simpen
Keyword(s):  
Data Mining ◽  
Nearest Neighbor ◽  
Test Results ◽  
K Nearest Neighbor ◽  
Accuracy Rate ◽  
Sample Data ◽  
New Students ◽  
K Nearest Neighbor Algorithm ◽  
Using Data ◽  
Existing Data

Penelitian ini dimaksudkan untuk melakukan prediksi terhadap kemungkian mahasiswa baru dapat menyelesaikan studi tepat waktu dengan menggunakan analisis data mining untuk menggali tumpukan histori data dengan menggunakan algoritma K-Nearest Neighbor (KNN). Aplikasi yang dihasilkan pada penelitian ini akan menggunakan berbagai atribut yang klasifikasikan dalam suatu data mining antara lain nilai ujian nasional (UN), asal sekolah/ daerah, jenis kelamin, pekerjaan dan penghasilan orang tua, jumlah bersaudara, dan lain-lain sehingga dengan menerapkan analysis KNN dapat dilakukan suatu prediksi berdasarkan kedekatan histori data yang ada dengan data yang baru, apakah mahasiswa tersebut berpeluang untuk menyelesaikan studi tepat waktu atau tidak. Dari hasil pengujian dengan menerapkan algoritma KNN dan menggunakan data sampel alumni tahun wisuda 2004 s.d. 2010 untuk kasus lama dan data alumni tahun wisuda 2011 untuk kasus baru diperoleh tingkat akurasi sebesar 83,36%.This research is intended to predict the possibility of new students time to complete studies using data mining analysis to explore the history stack data using K-Nearest Neighbor algorithm (KNN). Applications generated in this study will use a variety of attributes in a data mining classified among other Ujian Nasional scores (UN), the origin of the school / area, gender, occupation and income of parents, number of siblings, and others that by applying the analysis KNN can do a prediction based on historical proximity of existing data with new data, whether the student is likely to complete the study on time or not. From the test results by applying the KNN algorithm and uses sample data alumnus graduation year 2004 s.d 2010 for the case of a long and alumni data graduation year 2011 for new cases obtained accuracy rate of 83.36%.

Comparative Responses to Squats Completed with Free Weights and an Exoskeleton

2020 ◽  
Vol 4 (1) ◽  
pp. 51-63
Author(s):  
Peter Neuhaus ◽  
Chris Jumonville ◽  
Rachel A. Perry ◽  
Roman Edwards ◽  
Jake L. Martin ◽  
...  
Keyword(s):  
Data Collection ◽  
Female Athletes ◽  
Pearson Correlation ◽  
Correlation Coefficients ◽  
Actual Data ◽  
Limited Data ◽  
Comparative Similarity ◽  
Insufficient Amount ◽  
Force Data ◽  
Familiarization Session

AbstractTo assess the comparative similarity of squat data collected as they wore a robotic exoskeleton, female athletes (n=14) did two exercise bouts spaced 14 days apart. Data from their exoskeleton workout was compared to a session they did with free weights. Each squat workout entailed a four-set, four-repetition paradigm with 60-second rest periods. Sets for each workout involved progressively heavier (22.5, 34, 45.5, 57 kg) loads. The same physiological, perceptual, and exercise performance dependent variables were measured and collected from both workouts. Per dependent variable, Pearson correlation coefficients, t-tests, and Cohen's d effect size compared the degree of similarity between values obtained from the exoskeleton and free weight workouts. Results show peak O2, heart rate, and peak force data produced the least variability. In contrast, far more inter-workout variability was noted for peak velocity, peak power, and electromyography (EMG) values. Overall, an insufficient amount of comparative similarity exists for data collected from both workouts. Due to the limited data similarity, the exoskeleton does not exhibit an acceptable degree of validity. Likely the cause for the limited similarity was due to the brief amount of familiarization subjects had to the exoskeleton prior to actual data collection. A familiarization session that accustomed subjects to squats done with the exoskeleton prior to actual data collection may have considerably improved the validity of data obtained from that device.

Leader-Member Exchange (LMX) Impact on Work-Home Interference in a South African Commuter Transport Engineering Company

2019 ◽  
Vol 43 ◽  
Author(s):  
Jan Christoff Visagie ◽  
Michael M. Jones ◽  
Herman L. Linde
Keyword(s):  
South African ◽  
Structural Equation ◽  
Pearson Correlation ◽  
Correlation Coefficients ◽  
Leader Member Exchange ◽  
Negative Consequences ◽  
Dyadic Relationship ◽  
Cross Sectional ◽  
Engineering Company ◽  
Member Exchange

The South African workplace is confronted with many leadership challenges, specifically those relating to the employment relationship between subordinates and their supervisors. A high-quality relationship is essential, considering the work-family spillovers employees experience. Limited research has been conducted on the potential positive and negative consequences of the leader-member exchange (LMX) dyadic relationship. In this study, we used a cross-sectional research design, and drew an employee sample (N = 120) from a commuter transport engineering company. A five-point Likert scale was employed and statistical analyses were carried out using the SAS statistical program. We calculated Pearson correlation coefficients and used structural equation modelling to test the proposed conceptual model to indicate possible correlations between the different variables. The main finding of the study was that the nature of the LMX relationship quality in the relevant company appeared to be high and positively related to work-home enrichment but negatively related to work-home conflict and role overload. The article concludes by making a number of suggestions to respond to challenges.

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