Resampling-based Classification Using Depth for Functional Curves

As knowledge of genetics and genome elements increases, the demand for the development of bioinformatics tools for analyzing these data is raised. Riboswitches are genetic components, usually located in the untranslated regions of mRNAs, that regulate gene expression. Additionally, their interaction with antibiotics has been recently suggested, implying a role in antibiotic effects and resistance. Following a previously published sequential block finding algorithm, herein, we report the development of a new block location-based feature extraction strategy (BLBFE). This procedure utilizes the locations of family-specific sequential blocks on riboswitch sequences as features. Furthermore, the performance of other feature extraction strategies, including mono- and dinucleotide frequencies, k-mer, DAC, DCC, DACC, PC-PseDNC-General and SC-PseDNC-General methods, was investigated. KNN, LDA, naïve Bayes, PNN and decision tree classifiers accompanied by V-fold cross-validation were applied for all methods of feature extraction, and their performances based on the defined feature extraction strategies were compared. Performance measures of accuracy, sensitivity, specificity and F-score for each method of feature extraction were studied. The proposed feature extraction strategy resulted in classification of riboswitches with an average correct classification rate (CCR) of 90.8%. Furthermore, the obtained data confirmed the performance of the developed feature extraction method with an average accuracy of 96.1%, an average sensitivity of 90.8%, an average specificity of 97.52% and an average F-score of 90.69%. Our results implied that the proposed feature extraction (BLBFE) method can classify and discriminate riboswitch families with high CCR, accuracy, sensitivity, specificity and F-score values.

This study tested the possibility of using non-targeted analysis of volatile organic compounds by headspace solid-phase microextraction-gas chromatography-mass spectrometry coupled with chemometrics as a potential tool for differentiating leg meat of oat- and wheat-fed (ad libitum) White Kołuda geese. Thirty-six classification models were obtained for which the correct classification rate and classification accuracy for oatfed and wheat-fed geese were calculated based on a seven-fold cross-validation. Generally, the most advantageous method of the sample preparation was the high-temperature heat treatment version, whereas the highest correct classification rate was obtained when the chemometric analysis was carried out on the female, then male, and finally male + female variant of group comparisons (P<0.01). Furthermore, log-transformation appeared to be a slightly better data preprocessing technique in comparison to systematic ratio normalization. The obtained classification models can potentially differentiate the meat of oat-fattened from wheat-fattened White Kołuda geese.

To evaluate the sensitivity and specificity of arthroscopy and arthrotomy for diagnosis of medial meniscal pathology and to evaluate the diagnostic value of medial meniscal probing. Ex vivo study. Cadaveric canine stifle joints (n=30). Stifle joints were assigned to either a cranial cruciate ligament (CrCL) deficient or intact group. Within each stifle joint, no medial meniscal tear, a peripheral detachment, or 1 of 3 variants of vertical longitudinal tears of the medial meniscus were created. Each stifle joint had arthroscopy, craniomedial (CrMed), and caudomedial (CdMed) arthrotomy. Diagnoses were made by both observation and probing. Sensitivity, specificity, and correct classification rate (CCR) for diagnosing the state of the medial meniscus using both observation and probing with all diagnostic methods were calculated. Odds ratios were calculated to determine if probing increased diagnostic accuracy. Arthroscopy with probing was the most sensitive and specific diagnostic method and had the highest CCR. For arthrotomy, CrMed was the most sensitive in CrCL-deficient and CdMed the most sensitive in stable, CrCL-intact stifle joints. For all methods, probing increased their diagnostic accuracy. Arthroscopy is the most accurate diagnostic method; however, probing the medial meniscus enhances the diagnostic accuracy of all methods. Accurate diagnosis of medial meniscal pathology is ideally achieved by means of arthroscopy; however, if arthrotomy is chosen, CrMed should be selected in unstable and CdMed in stable stifle joints. Regardless, medial meniscal probing should be performed to increase diagnostic accuracy.

Combination of spectra and texture data of hyperspectral imaging for differentiating between free-range and broiler chicken meats

The human voice comes through the vocal cords. The voice can be divided into the lung, the vocal cords, and the larynx. Human emotions can be detected through the voice, and because of that, it is important to process the human voice. Sadness and happiness, as well as truthfulness and deceitfulness, can be detected through the voice. The use of these methods could reduce crime and etc. Data recorded from a variety of actors were used to detect emotions portrayed by human voices. The k nearest neighbor (KNN), Bayesian, and radial basis function (RBF) methods were used to detect emotions by training and testing algorithms based on the data. The KNN method had a low correct classification rate (CCR), and the RBF method had a high CCR.

Urban sprawl phenomenon that occurred in DKI Jakarta — Indonesia led to the expansion of the surrounding areas. Urban sprawl is a process of physical exposure to the outer city physical appearance caused by the rapid economic development and population growth. The “Bodetabek” area has turned into a densely populated residence and even an industrial estate. The compromise makes citizens of Bodetabek and DKI Jakarta leave their house to carry out their activities outside the administrative area of their residence and back on that day. People who do this kind of migration are called commuters. Their routine movement is the main cause of traffic jam. The transition of commuter transport modes from private vehicles to public transport is a solution to reduce congestion. However, not all commuters are interested in using public transportation. The results from the 2014 Commuter's Survey show that there is only 27 percent of Jabodetabek commuters use public transportation to go to their destination. Bayesian Network (BN) and logistic regression are proposed to model this kind of transportation choice in Jabodetabek. Logistic regression is widely used in classification modeling. While BN, including Naive Bayes (NB) and Hierarchical Naive Bayes (HNB), is a capable model of explaining the structure of relationships between complex random variables into diagrammatic forms based on conditional probability theory. The results show that Polytomous Logistic Regression has the highest Correct Classification Rate (CCR) and Area Under (a ROC) Curve (AUC). The Polytomous Logistic Regression, however, need more computational time consuming than NB and HNB.

Influence of geographical location, crop type and crop residue cover on bacterial and fungal community structures

The status of axillary lymph node metastases determines the treatment and overall survival of breast cancer (BC) patients. Three-dimensional (3D) assessment methods have advantages for spatial localization and are more responsive to morphological changes in lymph nodes than two-dimensional (2D) assessment methods, and we speculate that methods developed using 3D reconstruction systems have high diagnostic efficacy. This exploratory study included 43 patients with histologically confirmed BC diagnosed at Second Xiangya Hospital of Central South University between July 2017 and August 2020, all of whom underwent preoperative CT scans. Patients were divided into a training cohort to train the model and a validation cohort to validate the model. A 3D axillary lymph node atlas was constructed on a 3D reconstruction system to create various methods of assessing lymph node metastases for a comparison of diagnostic efficacy. Receiver operating characteristic (ROC) curve analysis was performed to assess the diagnostic values of these methods. A total of 43 patients (mean [SD] age, 47 [10] years) met the eligibility criteria and completed 3D reconstruction. An axillary lymph node atlas was established, and a correlation between lymph node sphericity and lymph node metastasis was revealed. By continuously fitting the size and characteristics of axillary lymph nodes on the 3D reconstruction system, formulas and models were established to determine the presence or absence of lymph node metastasis, and the 3D method had better sensitivity for axillary lymph node assessment than the 2D method, with a statistically significant difference in the correct classification rate. The combined diagnostic method was superior to a single diagnostic method, with a 92.3% correct classification rate for the 3D method combined with ultrasound. In addition, in patients who received neoadjuvant chemotherapy (NAC), the correct classification rate of the 3D method (72.7%) was significantly higher than that of ultrasound (45.5%) and CT (54.5%). By establishing an axillary lymph node atlas, the sphericity formula and model developed with the 3D reconstruction system achieve a high correct classification rate when combined with ultrasound or CT and can also be applied to patients receiving NAC.

In this work we develop a fast facial expression recognition system based on cross correlation with low complexity by proposing a method that does not need face detection for facial points tracking. Moreover, our simple feature selection according to the facial characteristics differentiates between the six basic expressions (happiness, surprise, sadness, disgust, fear and anger). In this system, 20 selected facial feature points from the first frame to the last are tracked automatically using a cross-correlation optical flow. The extracted feature vector is then given to following classifiers: Bayes optimal classifier with two approaches in probability density function estimation, K-nearest neighbor and support vector machine with radial basis function kernel. These classifiers are analyzed according to their correct classification rate by the cross validation method. For Cohn-Kanade database the best result is obtained by Bayes optimal classifier with the average correct classification rate (Ave-CCR) of 89.67%.

Predictive fish distribution models have utility in planning conservation measures for rare fish species. However, species rarity creates sampling and modeling difficulties that require an understanding of model accuracy. We evaluated existing distribution models for 10 rare fishes based on 2,026 community fish samples and associated riverine habitat. Our fieldwork provided an independent fish species inventory for 143 sample sites. This inventory was used to quantify species detectability for use as a weighting factor with which to correct false-negative modeling errors. Presence/absence data were compared with predictions to evaluate model accuracy as determined by Cohen's kappa and correct classification rates. Detection probabilities were generally small but ranged from 0 to 0.68. The models predicted species occurrence with relatively high success (average kappa = 45.6% and average correct classification rate = 74.1%). The habitat variables for predicting species occurrence varied among species; however, stream size and streamflow were the most influential. All distribution models had adequate predictive abilities and improved our understanding of fish distributions and the factors determining those distributions. Model accuracy statistics can provide managers with a measure of confidence when they are directing conservation activities.

Deep learning approaches for force feedback based void defect detection in friction stir welding

Fourier transform infrared (FT-IR) spectroscopy and chemometrics were used to verify the origin of honey samples (n=150) from Europe and South America. Authentic honey samples were collected from five sources, namely unfiltered samples from Mexico in 2004, commercially filtered samples from Ireland and Argentina in 2004, commercially filtered samples from the Czech Republic in 2005 and 2006, and commercially filtered samples from Hungary in 2006. Samples were diluted with distilled water to a standard solids content (70 degrees Brix) and their spectra (2500-12 500 nm) recorded at room temperature using an FT-IR spectrometer equipped with a germanium attenuated total reflection (ATR) accessory. First- and second-derivative and standard normal variate (SNV) data pretreatments were applied to the recorded spectra, which were analyzed using partial least squares (PLS) regression analysis, factorial discriminant analysis (FDA), and soft independent modeling of class analogy (SIMCA). In general, when an attenuated wavelength range (6800-11 500 nm) rather than the whole spectrum (2500-12 500 nm) was studied, higher correct classification rates were achieved. An overall correct classification of 93.3% was obtained for honeys by PLS discriminant analysis, while FDA techniques correctly classified 94.7% of honey samples. Correct classifications of up to 100% were achieved using SIMCA, but models describing some classes had very high false positive rates.

Most supervised classification methods are limited by the requirement for more cases than variables. In microarray data the number of variables (genes) far exceeds the number of cases (arrays), and thus filtering and pre-selection of genes is required. We describe the application of Between Group Analysis (BGA) to the analysis of microarray data. A feature of BGA is that it can be used when the number of variables (genes) exceeds the number of cases (arrays). BGA is based on carrying out an ordination of groups of samples, using a standard method such as Correspondence Analysis (COA), rather than an ordination of the individual microarray samples. As such, it can be viewed as a method of carrying out COA with grouped data. We illustrate the power of the method using two cancer data sets. In both cases, we can quickly and accurately classify test samples from any number of specified a priori groups and identify the genes which characterize these groups. We obtained very high rates of correct classification, as determined by jack-knife or validation experiments with training and test sets. The results are comparable to those from other methods in terms of accuracy but the power and flexibility of BGA make it an especially attractive method for the analysis of microarray cancer data.

Application and validation of a new approach for modelling benthic invertebrate dispersal: First colonisation of a former open sewer system

Background: Cardiovascular diseases are the leading cause of death worldwide. The search for safe solutions to ensure priority in the healthcare of patients with severe conditions, such as acute coronary syndrome, is a determining factor in the prognosis. Objective: To evaluate the healthcare of patients with acute coronary syndrome undergoing the Manchester triage system applied by nurses. Methods: Retrospective observational study based on the review of medical reports of patients admitted for acute coronary syndrome between October 2010 and April 2012 in a public hospital of cardiology in Joinville, Brazil. The following were assessed: health history, symptoms on admission, medical diagnosis, classification by the Manchester triage system, opening hours and procedures, information on admission, and the outcome of the case. The data were entered into an e-form and submitted to statistical analysis. Results: Of the 191 patients selected, 65.0% were men and 47.0% had a history of coronary artery disease. The Manchester triage system allowed correctly classifying 80.1% of patients. The flowchart “thorax pain” with the discriminator “chest pain” was the most used. The average waiting time for classification, medical care and first electrocardiogram were 12.2, 28.6 and 24.9 minutes, respectively. Conclusion: The Manchester triage system allowed a high rate of correct classification by the nurse of patients with acute coronary syndrome.