Linear Discriminant Analysis Classifier Research Articles

An efficient approach for EMG controlled pattern recognition system based on MUAP identification and segregation

An Electromyography (EMG) based pattern recognition system constitutes various steps of signal processing and control engineering from signal acquisition to real-time control. Efficient control of external devices largely depends on the signal processing steps executed before the final output. This work presents a new approach to signal processing using Motor Unit Action Potential (MUAP) based signal decomposition and segmentation. An MUAP is a neurological response during muscle contraction. Due to the higher contact area of surface electrodes, MUAPs from multiple muscles are captured. An MUAP generated from a single muscle usually has identical waveshapes and similar discharging rates and usually lasts for 8–15 ms. These are known as primary MUAPs. The proposed algorithm identifies and uses the primary observed MUAPs for feature extraction and classification. Firstly, noise signals are eliminated by a determined noise margin, which also separates the active muscle movement signals. Next, a novel MUAP identification algorithm is implemented to detect the MUAP trains. Then, identified primary MUAPs are used to make segments with variable widths to extract feature vectors. Based on the correlation score of all the primary MUAPs, the segmentation is performed, which results in segmentation width varying from 110–200 ms. The achieved segmentation width is lesser than the conventional overlapping and non-overlapping methods — the proposed approach results in a 20 to 50% reduction in the segmentation width. Four different classifiers are tested during the machine learning stage to investigate the performance of the proposed approach. The obtained feature sets are then used to train the Linear Discriminant Analysis (LDA), K-Nearest Neighbor (kNN), Decision Tree (DT), and Random Forest (RF) classifiers. The classifiers are tested with precision, recall, F1 score, and accuracy. The kNN and DT classifiers performed better than the LDA and RF classifiers. The maximum precision and recall are 100% while the maximum achieved accuracy is 98.56%. The comparative results show higher accuracy even at lower segmentation widths than the conventional constant window scheme. The kNN and DT classifiers provide a 5% to 15% increment in accuracy compared to the constant window segmentation-based approach.

An integrated machine learning approach for evaluating critical success factors influencing project portfolio management adoption in the construction industry

PurposeIn today’s intricate and dynamic construction sector, traditional project management techniques, which view projects in isolation, are no longer sufficient. Project Portfolio Management (PPM) has proven to be an efficient alternative solution for handling multiple construction projects. As such, based on a Machine Learning (ML) approach, this study aims to explore the Critical Success Factors (CSFs) influencing the adoption of PPM, aiming to enhance PPM implementation in construction projects.Design/methodology/approachA questionnaire based on CSFs gathered from prior studies was developed and validated by experts. Afterward, exploratory data analysis is conducted to understand CSF–PPM relationships. Preprocessing techniques ensure uniformity in variable magnitudes. Lastly, ML techniques, namely Linear Discriminant Analysis (LDA), Logistic Regression (LR) and Extra Trees Classifier (ETC) are developed to model and investigate CSFs' impact on PPM adoption.FindingsThe findings pointed out that the ETC model marginally outperforms other ML models with a classification accuracy of 93%. Also, the project size, utilized PPM tool and resource allocation-related factors are the most significant CSFs that influenced the PPM performance by about 48.5%.Originality/valueThis study contributes to the existing body of knowledge by raising awareness among construction companies and other project stakeholders about the pivotal CSFs that must be considered when prioritizing projects and designing an optimal PPM approach.

Radiomics and quantitative multi-parametric MRI for predicting uterine fibroid growth.

Uterine fibroids (UFs) can pose a serious health risk to women. UFs are benign tumors that vary in clinical presentation from asymptomatic to causing debilitating symptoms. UF management is limited by our inability to predict UF growth rate and future morbidity. We aim to develop a predictive model to identify UFs with increased growth rates and possible resultant morbidity. We retrospectively analyzed 44 expertly outlined UFs from 20 patients who underwent two multi-parametric MR imaging exams as part of a prospective study over an average of 16 months. We identified 44 initial features by extracting quantitative magnetic resonance imaging (MRI) features plus morphological and textural radiomics features from DCE, T2, and apparent diffusion coefficient sequences. Principal component analysis reduced dimensionality, with the smallest number of components explaining over 97.5% of the variance selected. Employing a leave-one-fibroid-out scheme, a linear discriminant analysis classifier utilized these components to output a growth risk score. The classifier incorporated the first three principal components and achieved an area under the receiver operating characteristic curve of 0.80 (95% confidence interval [0.69; 0.91]), effectively distinguishing UFs growing faster than the median growth rate of from slower-growing ones within the cohort. Time-to-event analysis, dividing the cohort based on the median growth risk score, yielded a hazard ratio of 0.33 [0.15; 0.76], demonstrating potential clinical utility. We developed a promising predictive model utilizing quantitative MRI features and principal component analysis to identify UFs with increased growth rates. Furthermore, the model's discrimination ability supports its potential clinical utility in developing tailored patient and fibroid-specific management once validated on a larger cohort.

Wearable Surface Deformation Myography (sDMG) System for Recognition of Locomotion Modes.

This study designs a wearable sensing system for locomotion mode recognition using lower-limb skin surface curvature deformation caused by the morphological changes of musculotendinous complexes and soft tissues. Flexible bending sensors are embedded into stretch pants, enabling curvature deformations of specific skin segments above lower-limb muscle groups to be captured in a noncontact manner. To evaluate the performance of this system, we conducted experiments on eight able-bodied subjects completing seven common locomotive activities, including walking, running, ramp ascending/descending, stair ascending/descending, and standing. The system measured seven channels of deformation signals from two cross-sections on the shank and the thigh. The collected signals were distinguishable across different locomotion modes and exhibited consistency when monitoring steps. Using selected time-domain features and a linear discriminant analysis (LDA) classifier enabled the proposed system to continuously recognize locomotion modes with an average accuracy of 96.5%. Furthermore, the system maintains recognition performance with 95.7% accuracy even after removing and reapplying the sensors. Finally, we conducted comparison experiments to analyze how window length, feature selection, and the number of channels affect recognition performance, providing insights for optimization. We believe that this novel signal platform holds great potential as a valuable supplementary tool in wearable human motion detection, enriching the information diversity for motion analysis, and enabling new possibilities for further advancements and applications in fields including biomedical engineering, textiles, and computer graphics.

Dopamine and deep brain stimulation accelerate the neural dynamics of volitional action in Parkinson's disease.

The ability to initiate volitional action is fundamental to human behaviour. Loss of dopaminergic neurons in Parkinson's disease is associated with impaired action initiation, also termed akinesia. Both dopamine and subthalamic deep brain stimulation (DBS) can alleviate akinesia, but the underlying mechanisms are unknown. An important question is whether dopamine and DBS facilitate de novo build-up of neural dynamics for motor execution or accelerate existing cortical movement initiation signals through shared modulatory circuit effects. Answering these questions can provide the foundation for new closed-loop neurotherapies with adaptive DBS, but the objectification of neural processing delays prior to performance of volitional action remains a significant challenge. To overcome this challenge, we studied readiness potentials and trained brain signal decoders on invasive neurophysiology signals in 25 DBS patients (12 female) with Parkinson's disease during performance of self-initiated movements. Combined sensorimotor cortex electrocorticography and subthalamic local field potential recordings were performed OFF therapy (n = 22), ON dopaminergic medication (n = 18) and on subthalamic deep brain stimulation (n = 8). This allowed us to compare their therapeutic effects on neural latencies between the earliest cortical representation of movement intention as decoded by linear discriminant analysis classifiers and onset of muscle activation recorded with electromyography. In the hypodopaminergic OFF state, we observed long latencies between motor intention and motor execution for readiness potentials and machine learning classifications. Both, dopamine and DBS significantly shortened these latencies, hinting towards a shared therapeutic mechanism for alleviation of akinesia. To investigate this further, we analysed directional cortico-subthalamic oscillatory communication with multivariate granger causality. Strikingly, we found that both therapies independently shifted cortico-subthalamic oscillatory information flow from antikinetic beta (13-35 Hz) to prokinetic theta (4-10 Hz) rhythms, which was correlated with latencies in motor execution. Our study reveals a shared brain network modulation pattern of dopamine and DBS that may underlie the acceleration of neural dynamics for augmentation of movement initiation in Parkinson's disease. Instead of producing or increasing preparatory brain signals, both therapies modulate oscillatory communication. These insights provide a link between the pathophysiology of akinesia and its' therapeutic alleviation with oscillatory network changes in other non-motor and motor domains, e.g. related to hyperkinesia or effort and reward perception. In the future, our study may inspire the development of clinical brain computer interfaces based on brain signal decoders to provide temporally precise support for action initiation in patients with brain disorders.

A Comprehensive Review on Discriminant Analysis for Addressing Challenges of Class-Level Limitations, Small Sample Size, and Robustness

The classical linear discriminant analysis (LDA) algorithm has three primary drawbacks, i.e., small sample size problem, sensitivity to noise and outliers, and inability to deal with multi-modal-class data. This paper reviews LDA technology and its variants, covering the taxonomy and characteristics of these technologies and comparing their innovations and developments in addressing these three shortcomings. Additionally, we describe the application areas and emphasize the kernel extensions of these technologies to solve nonlinear problems. Most importantly, this paper presents perspectives on future research directions and potential research areas in this field.

Non-destructive prediction of fertility and sex in chicken eggs using the short wave near-infrared region

The objective of this study was to evaluate the ability of a handheld near-infrared device (900–1600 nm) to predict fertility and sex (male and female) traits in-ovo. The NIR reflectance spectra of the egg samples were collected on days 0, 7, 14 and 18 of incubation and the data was analysed using principal component analysis (PCA), linear discriminant analysis (LDA) and support vector machines classification (SVM). The overall classification rates for the prediction of fertile and infertile egg samples ranged from 73 % to 84 % and between 93 % to 95 % using LDA and SVM classification, respectively. The highest classification rate was obtained on day 7 of incubation. The classification between male and female embryos achieved lower classification rates, between 62 % and 68 % using LDA and SVM classification, respectively. Although the classification rates for in-ovo sexing obtained in this study are higher than those obtained by chance (50 %), the classification results are currently not sufficient for industrial in-ovo sexing of chicken eggs. These results demonstrated that short wavelengths in the NIR range may be useful to distinguish between fertile and infertile egg samples at days 7 and 14 during incubation.

A simplified method for relapsing-remitting multiple sclerosis detection: Insights from resting EEG signals

Background and Objective:Multiple sclerosis (MS) is a neurodegenerative autoimmune disease affecting the central nervous system, leading to various neurological symptoms. Early detection is paramount to prevent enduring damage during MS episodes. Although magnetic resonance imaging (MRI) is a common diagnostic tool, this study aims to explore the feasibility of using electroencephalography (EEG) signals for MS detection, considering their accessibility and ease of application compared to MRI. Methods:The study involved the analysis of EEG signals during rest from 17 MS patients and 27 healthy volunteers to investigate MS-healthy patterns. Power spectral density features (PSD) were extracted from the 32-channel EEG signals. The study employed Linear Discriminant Analysis (LDA), Support Vector Machine (SVM), Classification and Regression Trees (CART), and k-Nearest Neighbor (kNN) classifiers to identify channels with the highest accuracy. Notably, the study achieved 100% accuracy in MS detection using the “Fp1” and “Pz” channels with the LDA classifier. A statistical analysis, utilizing the independent sample t-test, was conducted to explore whether PSD features of these channels differed significantly between healthy individuals and those with MS. Results:The results of the study demonstrate that effective detection of MS can be achieved using PSD features from only two channels of the EEG signal. Specifically, the “Fp1” and “Pz” channels exhibited 100% accuracy in MS detection with the LDA classifier. The statistical analysis further explored and confirmed the significant differences in PSD features between healthy individuals and MS patients. Conclusion:The study concludes that the proposed method, utilizing PSD features from specific EEG channels, offers a straightforward and efficient diagnostic approach for the effective detection of MS. The findings suggest the potential utility of EEG signals as a non-invasive and accessible alternative for MS detection, highlighting the importance of further research in this direction.

Designing a Motion-Onset Visual-Evoked Potential-Based Brain–Computer Interface to Control a Computer Game

Brain-computer interface (BCI) establishes an alternative direct communication channel between the brain and computer or other external devices. Motion-onset visual evoked potential (mVEP) related to the individual perception of movement is among synchronous BCI system that utilizes external stimulus. mVEPs stimulation paradigm is like an oddball stimulation paradigm. Researchers have recently been investigating mVEPs to control a computer game. To control a computer game, they used translation stimulus to evoke these potentials in EEG brain signals. In this study, we examine designing a computer game with four different types of stimuli, including translation, contraction, expansion, and rotation. We also used time-domain and wavelet coefficients for the feature extraction. Also, we used different types of linear discriminant analysis (LDA) classification methods, including least absolute shrinkage and selection operator (LASSO), stepwise LDA (SWLDA); moreover, and spatial-temporal discriminant analysis (STDA) for detecting the target stimulus. Our results demonstrate that contraction, expansion, and rotation stimuli, as well as translation, could be employed to control a BCI game. Our results showed that subjects got higher scores, game accuracy control, and information transfer rate using the rotation stimulus with SWLDA/STDA classification methods.

Virtual reconstruction and geometric morphometric analysis of the Kocabaş hominin fossil from Turkey: Implications for taxonomy and evolutionary significance

The Kocabaş specimen comes from a travertine quarry near the homonymous village in the Denizli basin (Turkey). The specimen comprises three main fragments: portions of the right and left parietal and left and right parts of the frontal bone. The fossil was assumed to belong to the Homo erectus s.l. hypodigm by some authors, whereas others see similarities with Middle Pleistocene fossils (Broken Hill 1/Kabwe, Bodo, or Ceprano). Here, we present the first attempt to make a complete reconstruction of the missing medial portion of the frontal bone and a comprehensive geometric morphometric analysis of this bone. We restored the calotte by aligning and mirroring the three preserved fragments. Afterward, we restored the missing portion by applying the thin-plate spline interpolation algorithm of target fossils onto the reconstructed Kocabaş specimen. For the geometric morphometric analyses, we collected 80 landmarks on the frontal bone (11 osteometric points, 14 bilateral curve semilandmarks, and 41 surface semilandmarks). The comparative sample includes 21 fossils from different chronological periods and geographical areas and 30 adult modern humans from different populations. Shape analyses highlighted the presence in Kocabaş of features usually related to Middle Pleistocene Homo, such as a developed supraorbital torus associated with a relatively short frontal squama and reduced post-toral sulcus. Cluster analysis and linear discriminant analysis classification procedure suggest Kocabaş being part of the same taxonomic unit of Eurasian and African Middle Pleistocene Homo. In light of our results, we consider that attributing the Kocabaş hominin to H. erectus s.l. may be unwarranted. Results of our analyses are compatible with different evolutionary scenarios, but a more precise chronological framework is needed for a thorough discussion of the evolutionary significance of this specimen. Future work should clarify its geological age, given uncertainties regarding its stratigraphic provenance.

Leveraging machine learning for predicting acute graft-versus-host disease grades in allogeneic hematopoietic cell transplantation for T-cell prolymphocytic leukaemia

Orphan diseases, exemplified by T-cell prolymphocytic leukemia, present inherent challenges due to limited data availability and complexities in effective care. This study delves into harnessing the potential of machine learning to enhance care strategies for orphan diseases, specifically focusing on allogeneic hematopoietic cell transplantation (allo-HCT) in T-cell prolymphocytic leukemia. The investigation evaluates how varying numbers of variables impact model performance, considering the rarity of the disease. Utilizing data from the Center for International Blood and Marrow Transplant Research, the study scrutinizes outcomes following allo-HCT for T-cell prolymphocytic leukemia. Diverse machine learning models were developed to forecast acute graft-versus-host disease (aGvHD) occurrence and its distinct grades post-allo-HCT. Assessment of model performance relied on balanced accuracy, F1 score, and ROC AUC metrics. The findings highlight the Linear Discriminant Analysis (LDA) classifier achieving the highest testing balanced accuracy of 0.58 in predicting aGvHD. However, challenges arose in its performance during multi-class classification tasks. While affirming the potential of machine learning in enhancing care for orphan diseases, the study underscores the impact of limited data and disease rarity on model performance.

A new sub-class linear discriminant for miniature spectrometer based food analysis

The well-known and extensively studied Linear Discriminant Analysis (LDA) can have its performance lowered in scenarios where data is not homoscedastic or not Gaussian. That is, the classical assumptions when LDA models are built are not applicable, and consequently LDA projections would not be able to extract the needed features to explain the intrinsic structure of data and for classes to be separated. As with many real word data sets, data obtained using miniature spectrometers can suffer from such drawbacks which would limit the deployment of such technology needed for food analysis. The solution presented in the paper is to divide classes into subclasses and to use means of sub classes, classes, and data in the suggested between classes scatter metric. Further, samples belonging to the same subclass are used to build a measure of within subclass scatterness. Such a solution solves the shortcoming of the classical LDA. The obtained results when using the proposed solution on food data and on general machine learning datasets show that the work in this paper compares well to and is very competitive with similar sub-class LDA algorithms in the literature. An extension to a Hilbert space is also presented; and the kernel version of the presented solution can be fused with its linear counter parts to yield improved classification rates.

Bacterial Disease Detection of Cherry Plant Using Deep Features

Although the cherry plant is widely grown in the world and Turkey, it is a fruit tree that is difficult to grow and maintain. It can be exposed to various pesticide diseases, especially during fruiting. Today, approaches based on expert reviews and analyses are used for the identification of these diseases. In addition, cherry producers are trying to detect diseases with their knowledge based on experience. Computer-aided agricultural analysis systems are also being developed depending on the rapid developments in technology. These systems help to monitor all processes from planting, cultivation, and harvesting of agricultural products and to make decisions to grow the products healthily. One of the most important issues to be detected and monitored with these systems is plant diseases. The features of the cherry plant disease will be determined by using a pre-trained convolutional neural network (CNN) model which is DarkNet-19, within the scope of this study. These machine learning-based features have been used for the detection of bacteria-based diseases commonly seen on the leaves of cherry plants. The acquired features are classified with Linear Discriminant Analysis, K-Nearest Neighbor, and Support Vector Machine classifiers to solve the multi-class problem including diseased (less and very) and healthy plants. The experimental results show that a success rate of 88.1% was obtained in the detection of the disease.

Effect of Sample Presentation on the Classification of Black Soldier Fly Larvae Using Near-Infrared Spectroscopy

Black soldier fly larvae (BSFL) (Hermetia illucens) reared on food waste streams are considered a sustainable source of protein in feed livestock diets. Recently, portable near-infrared spectroscopy (NIR) instruments have been assessed to monitor the consistency and quality of food waste streams used to feed black soldier fly larvae. During the application of NIR spectroscopy, sample presentation (e.g., drying, processing, particle size) plays an important role in the accuracy of the models developed (quantitative or qualitative analysis). The objective of this study was to evaluate the effect of sample presentation (number of larvae used during the scanning of BSFL) on the accuracy of classification models developed to trace the food waste stream (e.g., supermarket of childcare) used to feed the larvae. BSFL samples were sourced from two waste streams and scanned as half, 1, 2, or 3 larvae using an NIR portable instrument (MicroNIR, Viavi, Milpitas, California, USA). Principal component analysis (PCA) and linear discriminant analysis (LDA) were used to analyze the NIR data and to classify the samples according to the waste stream. The main differences in the NIR spectra of the BSFL samples associated with the number of larvae scanned were observed around 1200 nm, mainly associated with the C-H overtones (lipids). The classification results showed that high classification rates (>93%) were obtained regardless of the number of larvae scanned, ranging from 93% (using 0.5 larvae) to 100% (using 1, 2, or 3 larvae samples). Overall, the number of larvae scanned had minimal to no effect on the accuracy of the LDA classification models. The present study demonstrated that a portable NIR instrument can be suitable for an initial rapid classification or determination of the origin of the waste stream used to feed the BSFL.

Feature Importance Analysis and Machine Learning for Alzheimer’s Disease Early Detection: Feature Fusion of the Hippocampus, Entorhinal Cortex, and Standardized Uptake Value Ratio

Introduction: Alzheimer’s disease (AD) is a progressive neurological disorder characterized by mild memory loss and ranks as a leading cause of mortality in the USA, accounting for approximately 120,000 deaths per year. It is also the primary form of dementia. Early detection is critical for timely intervention as the neurodegenerative process often starts 15–20 years before cognitive symptoms manifest. This study focuses on determining feature importance in AD classification using fused texture features from 3D magnetic resonance imaging hippocampal and entorhinal cortex and standardized uptake value ratio (SUVR) derived from positron emission tomography (PET) images. Methods: To achieve this objective, we employed four distinct classifiers (Linear Support Vector Classification, Linear Discriminant Analysis, Logistic Regression, and Logistic Regression Classifier with Stochastic Gradient Descent Learning). These classifiers were used to derive both average and top-ranked importance scores for each feature based on their outputs. Our framework is designed to distinguish between two classes, AD-negative (or mild cognitive impairment stable [MCIs]) and AD-positive (or MCI conversion [MCIc]), using a probabilistic neural network classifier and the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database. Results: The findings from the feature importance highlight the crucial role of the GLCM texture features extracted from the hippocampus and entorhinal cortex, demonstrating their superior performance compared to the volume and SUVR. GLCM texture AD classification achieved approximately 90% sensitivity in identifying MCIc cases while maintaining low false positives (below 30%) when fused with other features. Moreover, the receiver operating characteristic curves validate the GLCMs’ superior performance in distinguishing between MCIs and MCIc. Additionally, fusing different types of features improved classification performance compared to relying solely on any single feature category. Conclusion: Our study emphasizes the pivotal role of GLCM texture features in early Alzheimer’s detection.

Analyzing lower body movements using machine learning to classify autistic children

Autism spectrum disorder is a neurological disorder that affects children at an early age and its symptoms appear in varying degrees. Early detection of ASD and follow-up of treatment by specialists help improve autistic people's lives and reduce the symptoms they suffer from. In the past few years, a group of studies conducted gait analysis to diagnose children with autism, and although the results of these studies showed weak motor skills of Autism Spectrum Disorder children (ASD) compared to their peers of Typically Developing children (TD), there are some challenges facing researchers in this field. The first is the ability to create A safe, effective, friendly environment that reduces tension in ASD children and helps classify children into ASD/TD effectively. And the second is the ability to create an automated ASD diagnosis system. In this work, a system based on machine learning and augmented reality technology has been proposed to create an effective and friendly interactive environment that helps diagnose ASD and classify children to ASD / TD. The movement of the lower limbs of 15 ASD children and 15 TD children are tracked via a marker-less technique through a Kinect v2 sensor when playing a specific game based on kicking the ball task, that is using augmented reality technology. Then the kinematics features are extracted through Linear Discriminant Analysis technology. To determine these features' ability in the classification process, three machine learning algorithms are used, namely Support Vector Machine, Linear Discriminant Analysis, and Decision Tree. The results showed that the obtained kinematics features can classify children into ASD/TD, where the Support Vector Machine classifier obtained a high accuracy of 96.7% with sensitivity equal to 93.3% and specificity equal to 100%. In addition, the Linear Discriminant Analysis classifier obtained less accuracy of 93.3% with sensitivity equal to 93.3% and specificity equal to 93.3% compared with the Support Vector Machine classifier. Where the Decision Tree classifier obtained the lowest accuracy of 80.0% with sensitivity equal to 73.3% and specificity equal to 86.7% compared with the Support Vector Machine and Linear Discriminant Analysis classifiers. In addition to that, when using Linear Discriminant Analysis to reduce dimensions and extract features, the accuracy, sensitivity, and specificity of the Support Vector Machine classifier were increased and achieved by 100%. Based on these results, it has been proven that the proposed method is effective in classifying children to ASD/TD.

On the Equivalence of Linear Discriminant Analysis and Least Squares Regression.

Studying the relationship between linear discriminant analysis (LDA) and least squares regression (LSR) is of great theoretical and practical significance. It is well-known that the two-class LDA is equivalent to an LSR problem, and directly casting multiclass LDA as an LSR problem, however, becomes more challenging. Recent study reveals that the equivalence between multiclass LDA and LSR can be established based on a special class indicator matrix, but under a mild condition which may not hold under the scenarios with low-dimensional or oversampled data. In this article, we show that the equivalence between multiclass LDA and LSR can be established based on arbitrary linearly independent class indicator vectors and without any condition. In addition, we show that LDA is also equivalent to a constrained LSR based on the data-dependent indicator vectors. It can be concluded that under exactly the same mild condition, such two regressions are both equivalent to the null space LDA method. Illuminated by the equivalence of LDA and LSR, we propose a direct LDA classifier to replace the conventional framework of LDA plus extra classifier. Extensive experiments well validate the above theoretic analysis.

A compact Fourier-transform near-infrared spectrophotometer and chemometrics for characterizing a comprehensive set of seized ecstasy samples

A comprehensive data set of ecstasy samples containing MDMA (N-methyl-3,4-methylenedioxyamphetamine) and MDA (3,4-methylenedioxyamphetamine) seized by the Brazilian Federal Police was characterized using spectral data obtained by a compact, low-cost, near-infrared Fourier-transform based spectrophotometer. Qualitative and quantitative characterization was accomplished using soft independent modeling of class analogy (SIMCA), linear discriminant analysis (LDA) classification, discriminating partial least square (PLS-DA), and regression models based on partial least square (PLS). By applying chemometric analysis, a protocol can be proposed for the in-field screening of seized ecstasy samples. The validation led to an efficiency superior to 96 % for ecstasy classification and estimating total actives, MDMA, and MDA content in the samples with a root mean square error of validation of 4.4, 4.2, and 2.7 % (m/m), respectively. The feasibility and drawbacks of the NIR technology applied to ecstasy characterization and the compromise between false positives and false negatives rate achieved by the classification models are discussed and a new approach to improve the classification robustness was proposed considering the forensic context.

Exploring EEG-based motor imagery decoding: a dual approach using spatial features and spectro-spatial Deep Learning model IFNet.

In recent years, the decoding of motor imagery (MI) from electroencephalography (EEG) signals has become a focus of research for brain-machine interfaces (BMIs) and neurorehabilitation. However, EEG signals present challenges due to their non-stationarity and the substantial presence of noise commonly found in recordings, making it difficult to design highly effective decoding algorithms. These algorithms are vital for controlling devices in neurorehabilitation tasks, as they activate the patient's motor cortex and contribute to their recovery. This study proposes a novel approach for decoding MI during pedalling tasks using EEG signals. A widespread approach is based on feature extraction using Common Spatial Patterns (CSP) followed by a linear discriminant analysis (LDA) as a classifier. The first approach covered in this work aims to investigate the efficacy of a task-discriminative feature extraction method based on CSP filter and LDA classifier. Additionally, the second alternative hypothesis explores the potential of a spectro-spatial Convolutional Neural Network (CNN) to further enhance the performance of the first approach. The proposed CNN architecture combines a preprocessing pipeline based on filter banks in the frequency domain with a convolutional neural network for spectro-temporal and spectro-spatial feature extraction. To evaluate the approaches and their advantages and disadvantages, EEG data has been recorded from several able-bodied users while pedalling in a cycle ergometer in order to train motor imagery decoding models. The results show levels of accuracy up to 80% in some cases. The CNN approach shows greater accuracy despite higher instability.

Ligand and structure-based discovery of phosphorus-containing compounds as potential metalloproteinase inhibitors

ABSTRACT In this study, a methodology is proposed, combining ligand- and structure-based virtual screening tools, for the identification of phosphorus-containing compounds as inhibitors of zinc metalloproteases. First, we use Dragon molecular descriptors to develop a Linear Discriminant Analysis classification model, which is widely validated according to the OECD principles. This model is simple, robust, stable and has good discriminating power. Furthermore, it has a defined applicability domain and it is used for virtual screening of the DrugBank database. Second, docking experiments are carried out on the identified compounds that showed good binding energies to the enzyme thermolysin. Considering the potential toxicity of phosphorus-containing compounds, their toxicological profile is evaluated according to Protox II. Of the five molecules evaluated, two show carcinogenic and mutagenic potential at small LD50, not recommended as drugs, while three of them are classified as non-toxic, and could constitute a starting point for the development of new vasoactive metalloprotease inhibitor drugs. According to molecular dynamics simulation, two of them show stable interactions with the active site maintaining coordination with the metal. A high agreement is evident between QSAR, docking and molecular dynamics results, demonstrating the potentialities of the combination of these tools.