Qualitative discrimination and quantitative prediction of microplastics in ash based on near-infrared spectroscopy

Microplastics are recognized as a new environmental pollutant. Researchers have detected their presence in waste incineration ash. However, traditional testing methods take a very long testing period. There is a lack of research on detecting microplastics in waste incineration ash. In this paper, a portable near-infrared spectra (NIRS) spectrometer was used for qualitative discrimination and quantitative prediction of microplastics in ash. A total of 84 sets of simulated ash samples containing different types (PP, PS, PE, and PVC) and contents (2.4 wt% - 20 wt%) of microplastics were used in the model. The results show the qualitative discrimination model using support vector machines (SVM) method with multiplicative scatter correction (MSC) preprocessing could effectively identify the microplastic types in the ash with 100% detection accuracy. Furthermore, the partial least squares regression (PLSR) model was effective in quantitatively predicting the content of microplastics in ash. The Rp2 of the PP, PS, PE, and PVC models are 0.95, 0.93, 0.89, and 0.95, respectively. The RPD of the PP, PS, PE, and PVC models are 3.97, 3.96, 2.89 and 5.02, respectively. This study shows that microplastics in ash can be detected rapidly and accurately using portable near-infrared spectrometers.

Ensemble classifier fostered detection of arrhythmia using ECG data

Electrocardiogram (ECG) is a non-invasive medical tool that divulges the rhythm and function of the human heart. This is broadly employed in heart disease detection including arrhythmia. Arrhythmia is a general term for abnormal heart rhythms that can be identified and classified into many categories. Automatic ECG analysis is provided by arrhythmia categorization in cardiac patient monitoring systems. It aids cardiologists to diagnose the ECG signal. In this work, an Ensemble classifier is proposed for accurate arrhythmia detection using ECG Signal. Input data are taken from the MIT-BIH arrhythmia dataset. Then the input data was pre-processed using Python in Jupyter Notebook which run the code in an isolated manner and was able to keep code, formula, comments, and images. Then, Residual Exemplars Local Binary Pattern is applied for extracting statistical features. The extracted features are given to ensemble classifiers, like Support vector machines (SVM), Naive Bayes (NB), and random forest (RF) for classifying the arrhythmia as normal (N), supraventricular ectopic beat (S), ventricular ectopic beat (V), fusion beat (F), and unknown beat (Q). The proposed AD-Ensemble SVM-NB-RF method is implemented in Python. The proposed AD-Ensemble SVM-NB-RF method is 44.57%, 52.41%, and 29.49% higher accuracy; 2.01%, 3.33%, and 3.19% higher area under the curve (AUC); and 21.52%, 23.05%, and 12.68% better F-Measure compared with existing models, like multi-model depending on the ensemble of deep learning for ECG heartbeats arrhythmia categorization (AD-Ensemble CNN-LSTM-RRHOS), ECG signal categorization utilizing VGGNet: a neural network based classification method (AD-Ensemble CNN-LSTM) and higher performance arrhythmic heartbeat categorization utilizing ensemble learning along PSD based feature extraction method (AD-Ensemble MLP-NB-RF).

A comprehensive tool for rapid and accurate prediction of disease using DNA sequence classifier

In the current pandemic situation where the coronavirus is spreading very fast that can jump from one human to another. Along with this, there are millions of viruses for example Ebola, SARS, etc. that can spread as fast as the coronavirus due to the mobilization and globalization of the population and are equally deadly. Earlier identification of these viruses can prevent the outbreaks that we are facing currently as well as can help in the earlier designing of drugs. Identification of disease at a prior stage can be achieved through DNA sequence classification as DNA carries most of the genetic information about organisms. This is the reason why the classification of DNA sequences plays an important role in computational biology. This paper has presented a solution in which samples collected from NCBI are used for the classification of DNA sequences. DNA sequence classification will in turn gives the pattern of various diseases; these patterns are then compared with the samples of a newly infected person and can help in the earlier identification of disease. However, feature extraction always remains a big issue. In this paper, a machine learning-based classifier and a new technique for extracting features from DNA sequences based on a hot vector matrix have been proposed. In the hot vector representation of the DNA sequence, each pair of the word is represented using a binary matrix which represents the position of each nucleotide in the DNA sequence. The resultant matrix is then given as an input to the traditional CNN for feature extraction. The results of the proposed method have been compared with 5 well-known classifiers namely Convolution neural network (CNN), Support Vector Machines (SVM), K-Nearest Neighbor (KNN) algorithm, Decision Trees, Recurrent Neural Networks (RNN) on several parameters including precision rate and accuracy and the result shows that the proposed method gives an accuracy of 93.9%, which is highest compared to other classifiers.

Introspective analysis of convolutional neural networks for improving discrimination performance and feature visualisation

Deep neural networks have been widely explored and utilised as a useful tool for feature extraction in computer vision and machine learning. It is often observed that the last fully connected (FC) layers of convolutional neural network possess higher discrimination power as compared to the convolutional and maxpooling layers whose goal is to preserve local and low-level information of the input image and down sample it to avoid overfitting. Inspired from the functionality of local binary pattern (LBP) operator, this paper proposes to induce discrimination into the mid layers of convolutional neural network by introducing a discriminatively boosted alternative to pooling (DBAP) layer that has shown to serve as a favourable replacement of early maxpooling layer in a convolutional neural network (CNN). A thorough research of the related works show that the proposed change in the neural architecture is novel and has not been proposed before to bring enhanced discrimination and feature visualisation power achieved from the mid layer features. The empirical results reveal that the introduction of DBAP layer in popular neural architectures such as AlexNet and LeNet produces competitive classification results in comparison to their baseline models as well as other ultra-deep models on several benchmark data sets. In addition, better visualisation of intermediate features can allow one to seek understanding and interpretation of black box behaviour of convolutional neural networks, used widely by the research community.

Rapid and sensitive SERS detection of melamine in milk using Ag nanocube array substrate coupled with multivariate analysis

In this study, a facile Ag nanocube (NC) array substrate was fabricated for rapid SERS detection of melamine in milk. This easily-prepared substrate exhibited high Raman enhancement factor (~1.02 × 105) and good reproducibility with ~10.75% spot-to-spot variation in Raman intensity. Our proposed method can detect melamine as low as 0.01 ppm in standard solutions and 0.5 ppm in real milk samples after a simple one-step solvent extraction. Two multivariate analysis tools including partial least squares and support vector machines (SVM) were explored to develop reliable regression models for quantitative SERS analysis of melamine. By comparison, SVM regression models exhibited better predictive performance, especially in liquid milk, with root mean square error (RMSE) of calibration = 5.5783, coefficient of determination (R2) of calibration = 0.9807, RMSE of prediction = 1.9636, and R2 of prediction = 0.9736. Hence, this study offers a rapid and sensitive detection of adulterant melamine in milk samples.

Comparative evaluation of machine learning models for groundwater quality assessment

Contamination from pesticides and nitrate in groundwater is a significant threat to water quality in general and agriculturally intensive regions in particular. Three widely used machine learning models, namely, artificial neural networks (ANN), support vector machines (SVM), and extreme gradient boosting (XGB), were evaluated for their efficacy in predicting contamination levels using sparse data with non-linear relationships. The predictive ability of the models was assessed using a dataset consisting of 303 wells across 12 Midwestern states in the USA. Multiple hydrogeologic, water quality, and land use features were chosen as the independent variables, and classes were based on measured concentration ranges of nitrate and pesticide. This study evaluates the classification performance of the models for two, three, and four class scenarios and compares them with the corresponding regression models. The study also examines the issue of class imbalance and tests the efficacy of three class imbalance mitigation techniques: oversampling, weighting, and oversampling and weighting, for all the scenarios. The models' performance is reported using multiple metrics, both insensitive to class imbalance (accuracy) and sensitive to class imbalance (F1 score and MCC). Finally, the study assesses the importance of features using game-theoretic Shapley values to rank features consistently and offer model interpretability.

Quantitative Assessment of Speech in Cerebellar Ataxia Using Magnitude and Phase Based Cepstrum

The clinical assessment of speech abnormalities in Cerebellar Ataxia (CA) is time-consuming and inconsistent. We have developed an automated objective system to quantify CA severity and thereby facilitate remote monitoring and optimisation of therapeutic interventions. A quantitative acoustic assessment could prove to be a viable biomarker for this purpose. Our study explores the use of phase-based cepstral features extracted from the modified group delay function as a complement to the features obtained from the magnitude cepstrum. We selected a combination of 15 acoustic measurements using RELIEF feature selection algorithm during the feature optimisation process. These features were used to segregate ataxic speakers from normal speakers (controls) and objectively assess them based on their severity. The effectiveness of our study has been experimentally evaluated through a clinical study involving 42 patients diagnosed with CA and 23 age-matched controls. A radial basis function kernel based support vector machine (SVM) classifier achieved a classification accuracy of 84.6% in CA-Control discrimination [area under the ROC curve (AUC) of 0.97] and 74% in the modified 3-level CA severity estimation (AUC of 0.90) deduced from the clinical ratings. The strong classification ability of selected features and the SVM model supports this scheme's suitability for monitoring CA related speech motor abnormalities.

AIKYATAN: mapping distal regulatory elements using convolutional learning on GPU

Background

The data deluge can leverage sophisticated ML techniques for functionally annotating the regulatory non-coding genome. The challenge lies in selecting the appropriate classifier for the specific functional annotation problem, within the bounds of the hardware constraints and the model’s complexity. In our system Aikyatan, we annotate distal epigenomic regulatory sites, e.g., enhancers. Specifically, we develop a binary classifier that classifies genome sequences as distal regulatory regions or not, given their histone modifications’ combinatorial signatures. This problem is challenging because the regulatory regions are distal to the genes, with diverse signatures across classes (e.g., enhancers and insulators) and even within each class (e.g., different enhancer sub-classes).

Results

We develop a suite of ML models, under the banner Aikyatan, including SVM models, random forest variants, and deep learning architectures, for distal regulatory element (DRE) detection. We demonstrate, with strong empirical evidence, deep learning approaches have a computational advantage. Plus, convolutional neural networks (CNN) provide the best-in-class accuracy, superior to the vanilla variant. With the human embryonic cell line H1, CNN achieves an accuracy of 97.9% and an order of magnitude lower runtime than the kernel SVM. Running on a GPU, the training time is sped up 21x and 30x (over CPU) for DNN and CNN, respectively. Finally, our CNN model enjoys superior prediction performance vis-‘a-vis the competition. Specifically, Aikyatan-CNN achieved 40% higher validation rate versus CSIANN and the same accuracy as RFECS.

Conclusions

Our exhaustive experiments using an array of ML tools validate the need for a model that is not only expressive but can scale with increasing data volumes and diversity. In addition, a subset of these datasets have image-like properties and benefit from spatial pooling of features. Our Aikyatan suite leverages diverse epigenomic datasets that can then be modeled using CNNs with optimized activation and pooling functions. The goal is to capture the salient features of the integrated epigenomic datasets for deciphering the distal (non-coding) regulatory elements, which have been found to be associated with functional variants. Our source code will be made publicly available at: https://bitbucket.org/cellsandmachines/aikyatan.

Electronic supplementary material

The online version of this article (10.1186/s12859-019-3049-1) contains supplementary material, which is available to authorized users.

Detailed prediction of protein sub-nuclear localization

Background

Sub-nuclear structures or locations are associated with various nuclear processes. Proteins localized in these substructures are important to understand the interior nuclear mechanisms. Despite advances in high-throughput methods, experimental protein annotations remain limited. Predictions of cellular compartments have become very accurate, largely at the expense of leaving out substructures inside the nucleus making a fine-grained analysis impossible.

Results

Here, we present a new method (LocNuclei) that predicts nuclear substructures from sequence alone. LocNuclei used a string-based Profile Kernel with Support Vector Machines (SVMs). It distinguishes sub-nuclear localization in 13 distinct substructures and distinguishes between nuclear proteins confined to the nucleus and those that are also native to other compartments (traveler proteins). High performance was achieved by implicitly leveraging a large biological knowledge-base in creating predictions by homology-based inference through BLAST. Using this approach, the performance reached AUC = 0.70–0.74 and Q13 = 59–65%. Travelling proteins (nucleus and other) were identified at Q2 = 70–74%. A Gene Ontology (GO) analysis of the enrichment of biological processes revealed that the predicted sub-nuclear compartments matched the expected functionality. Analysis of protein-protein interactions (PPI) show that formation of compartments and functionality of proteins in these compartments highly rely on interactions between proteins. This suggested that the LocNuclei predictions carry important information about function. The source code and data sets are available through GitHub: https://github.com/Rostlab/LocNuclei.

Conclusions

LocNuclei predicts subnuclear compartments and traveler proteins accurately. These predictions carry important information about functionality and PPIs.

Electronic supplementary material

The online version of this article (10.1186/s12859-019-2790-9) contains supplementary material, which is available to authorized users.

Landslides-oriented urban disaster resilience assessment-A case study in ShenZhen, China

With the increasing expansion of cities associated with rapid urbanization, the ecological environment is being severely damaged, exposing cities to frequent extreme weather events. Urban ecological ecosystems are under great threat. Research on urban disaster resilience is conducive to a better understanding of disaster prevention and mitigation capacity, and provides valuable references for resilient city construction. In this study, a typical city under rapid urbanization in China - Shenzhen - was chosen as the research area, including the city's 57 sub-districts. Urban disaster resilience to rainfall-induced landslides was conceptually framed into the dimensions of physical resilience and social resilience. Support vector machine (SVM) was applied to evaluate the physical resilience and a Delphi-analytic hierarchy process (Delphi-AHP) model was used to assess social resilience on a sub-district scale in 2016. The results show that the physical resilience and social resilience of Shenzhen demonstrate obvious spatial concentration trends. Areas with low physical resilience were located in sub-districts of Dapeng New District with intense rainfall and complex topography, as well as those in Guangming New District with lateritic red earth derived from arenaceous shale. Areas with low social resilience were mainly located in eastern Shenzhen, including sub-districts in Longgang District and Dapeng New District, with undeveloped economy, inadequate infrastructures and many vulnerable people. All sub-districts in the three districts of Pingshan New District, Dapeng New District and Guangming New District need attention because of their low comprehensive resilience. Comparison of the physical resilience and social resilience indicated that the performance of physical resilience was significantly better than that of social resilience; only 26% of the sub-districts of Shenzhen had a higher level of social resilience than of physical resilience. Therefore, the government should strengthen urban management of social services and physical infrastructure provision to improve social resilience to cope with urban disasters.

A novel framework for intelligent surveillance system based on abnormal human activity detection in academic environments

Abnormal activity detection plays a crucial role in surveillance applications, and a surveillance system that can perform robustly in an academic environment has become an urgent need. In this paper, we propose a novel framework for an automatic real-time video-based surveillance system which can simultaneously perform the tracking, semantic scene learning, and abnormality detection in an academic environment. To develop our system, we have divided the work into three phases: preprocessing phase, abnormal human activity detection phase, and content-based image retrieval phase. For motion object detection, we used the temporal-differencing algorithm and then located the motions region using the Gaussian function. Furthermore, the shape model based on OMEGA equation was used as a filter for the detected objects (i.e., human and non-human). For object activities analysis, we evaluated and analyzed the human activities of the detected objects. We classified the human activities into two groups: normal activities and abnormal activities based on the support vector machine. The machine then provides an automatic warning in case of abnormal human activities. It also embeds a method to retrieve the detected object from the database for object recognition and identification using content-based image retrieval. Finally, a software-based simulation using MATLAB was performed and the results of the conducted experiments showed an excellent surveillance system that can simultaneously perform the tracking, semantic scene learning, and abnormality detection in an academic environment with no human intervention.

Multivariate modeling using quantitative CT metrics may improve accuracy of diagnosis of bronchiolitis obliterans syndrome after lung transplantation

BACKGROUND

To assess how quantitative CT (qCT) metrics compare to pulmonary function testing (PFT) and semi-quantitative image scores (SQS) to diagnose bronchiolitis obliterans syndrome (BOS), manifestation of chronic lung allograft dysfunction after lung transplantation (LTx), according to the type of LTx (unilateral or bilateral).

METHODS

Paired inspiratory-expiratory CT scans and PFTs of 176 LTx patients were analyzed retrospectively, and separated into BOS (78) and non-BOS (98) cohorts. SQS were assessed by 2 radiologists and graded (0-3) for features including mosaic attenuation and bronchiectasis. qCT metrics included lung volumes and air trapping volumes. Multivariate logistic regression (MVLR) and support vector machines (SVM) were used for the classification task.

RESULTS

MVLR and SVM models using PFT metrics demonstrated highest accuracy for bilateral LTx (max AUC 0.771), whereas models using qCT metrics-only outperformed models using SQS or PFTs in unilateral LTx (max AUC 0.817), to diagnose BOS. Adding PC (principal components) from qCT on top of PFT improved model diagnostic accuracy for all transplant types.

CONCLUSIONS

Combinations of qCT metrics augment the diagnostic performance of PFTs, are superior to SQS to predict BOS status, and outperform PFTs in the unilateral LTx group. This suggests that latent information on paired volumetric CT may allow early diagnosis of BOS in LTx patients, particularly in unilateral LTx.

"Noisy beets": impact of phenotyping errors on genomic predictions for binary traits in Beta vulgaris

BACKGROUND

Noise (errors) in scientific data is endemic and may have a detrimental effect on statistical analyses and experimental results. The effects of noisy data have been assessed in genome-wide association studies for case-control experiments in human medicine. Little is known, however, on the impact of noisy data on genomic predictions, a widely used statistical application in plant and animal breeding.

RESULTS

In this study, the sensitivity to noise in the data of five classification methods (K-nearest neighbours-KNN, random forest-RF, ridge logistic regression-LR, and support vector machines with linear or radial basis function kernels) was investigated. A sugar beet population of 123 plants phenotyped for a binary trait and genotyped for 192 SNP (single nucleotide polymorphism) markers was used. Labels (0/1 phenotype) were randomly sampled to generate noise. From the base scenario without errors in the labels, increasing proportions of noisy labels-up to 50 %-were generated and introduced in the data.

CONCLUSIONS

Local classification methods-KNN and RF-showed higher tolerance to noisy labels compared to methods that leverage global data properties-LR and the two SVM models. In particular, KNN outperformed all other classifiers with AUC (area under the ROC curve) higher than 0.95 up to 20 % noisy labels. The runner-up method, RF, had an AUC of 0.941 with 20 % noise.

Aesthetic preference recognition of 3D shapes using EEG

Recognition and identification of aesthetic preference is indispensable in industrial design. Humans tend to pursue products with aesthetic values and make buying decisions based on their aesthetic preferences. The existence of neuromarketing is to understand consumer responses toward marketing stimuli by using imaging techniques and recognition of physiological parameters. Numerous studies have been done to understand the relationship between human, art and aesthetics. In this paper, we present a novel preference-based measurement of user aesthetics using electroencephalogram (EEG) signals for virtual 3D shapes with motion. The 3D shapes are designed to appear like bracelets, which is generated by using the Gielis superformula. EEG signals were collected by using a medical grade device, the B-Alert X10 from advance brain monitoring, with a sampling frequency of 256 Hz and resolution of 16 bits. The signals obtained when viewing 3D bracelet shapes were decomposed into alpha, beta, theta, gamma and delta rhythm by using time-frequency analysis, then classified into two classes, namely like and dislike by using support vector machines and K-nearest neighbors (KNN) classifiers respectively. Classification accuracy of up to 80 % was obtained by using KNN with the alpha, theta and delta rhythms as the features extracted from frontal channels, Fz, F3 and F4 to classify two classes, like and dislike.

Automated classification of neurological disorders of gait using spatio-temporal gait parameters

OBJECTIVE

Automated pattern recognition systems have been used for accurate identification of neurological conditions as well as the evaluation of the treatment outcomes. This study aims to determine the accuracy of diagnoses of (oto-)neurological gait disorders using different types of automated pattern recognition techniques.

METHODS

Clinically confirmed cases of phobic postural vertigo (N = 30), cerebellar ataxia (N = 30), progressive supranuclear palsy (N = 30), bilateral vestibulopathy (N = 30), as well as healthy subjects (N = 30) were recruited for the study. 8 measurements with 136 variables using a GAITRite(®) sensor carpet were obtained from each subject. Subjects were randomly divided into two groups (training cases and validation cases). Sensitivity and specificity of k-nearest neighbor (KNN), naive-bayes classifier (NB), artificial neural network (ANN), and support vector machine (SVM) in classifying the validation cases were calculated.

RESULTS

ANN and SVM had the highest overall sensitivity with 90.6% and 92.0% respectively, followed by NB (76.0%) and KNN (73.3%). SVM and ANN showed high false negative rates for bilateral vestibulopathy cases (20.0% and 26.0%); while KNN and NB had high false negative rates for progressive supranuclear palsy cases (76.7% and 40.0%).

CONCLUSIONS

Automated pattern recognition systems are able to identify pathological gait patterns and establish clinical diagnosis with good accuracy. SVM and ANN in particular differentiate gait patterns of several distinct oto-neurological disorders of gait with high sensitivity and specificity compared to KNN and NB. Both SVM and ANN appear to be a reliable diagnostic and management tool for disorders of gait.