The potential of Raman hyperspectral imaging with a 785 nm excitation line laser was examined for the detection of aflatoxin contamination in corn kernels. Nine-hundred kernels were artificially inoculated in the laboratory, with 300 kernels each inoculated with AF13 (aflatoxigenic) fungus, AF36 (nonaflatoxigenic) fungus, and sterile distilled water (control). One-hundred kernels from each treatment were subsequently incubated for 3, 5, and 8 days. The mean spectra of single kernels were extracted from the endosperm side and the embryo area of the germ side, and local Raman peaks were identified based upon the calculated reference spectra of aflatoxin-negative and -positive categories separately. The principal component analysis-linear discriminant analysis models were established using different types of variable inputs including original full spectra, preprocessed full spectra, and identified local peaks over kernel endosperm-side, germ-side, and both sides. The results of the established discriminant models showed that the germ-side spectra performed better than the endosperm-side spectra. Based upon the 20 ppb-threshold, the best mean prediction accuracy of 82.6% was achieved for the aflatoxin-negative category using the original spectra in the combined form of both kernel sides, and the best mean prediction accuracy of 86.7% was obtained for the -positive category using the preprocessed germ-side spectra. Based upon the 100 ppb-threshold, the best mean prediction accuracies of 85.0% and 89.6% were achieved for the aflatoxin-negative and -positive categories separately, using the same type of variable inputs for the 20 ppb-threshold. In terms of overall prediction accuracy, the models established upon the original spectra in the combined form of both kernel sides achieved the best predictive performance, regardless of the threshold. The mean overall prediction accuracies of 81.8% and 84.5% were achieved with the 20 ppb- and 100 ppb-thresholds, respectively.

Pests are a significant challenge in paddy cultivation, resulting in a global loss of approximately 20 % of rice yield. Early detection of paddy insects can help to save these potential losses. Several ways have been suggested for identifying and categorizing insects in paddy fields, employing a range of advanced, noninvasive, and portable technologies. However, none of these systems have successfully incorporated feature optimization techniques with Deep Learning and Machine Learning. Hence, the current research provided a framework utilizing these techniques to detect and categorize images of paddy insects promptly. Initially, the suggested research will gather the image dataset and categorize it into two groups: one without paddy insects and the other with paddy insects. Furthermore, various pre-processing techniques, such as augmentation and image filtering, will be applied to enhance the quality of the dataset and eliminate any unwanted noise. To determine and analyze the deep characteristics of an image, the suggested architecture will incorporate 5 pre-trained Convolutional Neural Network models. Following that, feature selection techniques, including Principal Component Analysis (PCA), Recursive Feature Elimination (RFE), Linear Discriminant Analysis (LDA), and an optimization algorithm called Lion Optimization, were utilized in order to further reduce the redundant number of features that were collected for the study. Subsequently, the process of identifying the paddy insects will be carried out by employing 7 ML algorithms. Finally, a set of experimental data analysis has been conducted to achieve the objectives, and the proposed approach demonstrates that the extracted feature vectors of ResNet50 with Logistic Regression and PCA have achieved the highest accuracy, precisely 99.28 %. However, the present idea will significantly impact how paddy insects are diagnosed in the field.

This work proposes a functional data analysis approach for morphometrics in classifying three shrew species (S. murinus, C. monticola, and C. malayana) from Peninsular Malaysia. Functional data geometric morphometrics (FDGM) for 2D landmark data is introduced and its performance is compared with classical geometric morphometrics (GM). The FDGM approach converts 2D landmark data into continuous curves, which are then represented as linear combinations of basis functions. The landmark data was obtained from 89 crania of shrew specimens based on three craniodental views (dorsal, jaw, and lateral). Principal component analysis and linear discriminant analysis were applied to both GM and FDGM methods to classify the three shrew species. This study also compared four machine learning approaches (naïve Bayes, support vector machine, random forest, and generalised linear model) using predicted PC scores obtained from both methods (a combination of all three craniodental views and individual views). The analyses favoured FDGM and the dorsal view was the best view for distinguishing the three species.

UNASSIGNED: Spastic cerebral palsy, the most common pediatric-onset disabling condition with an estimated prevalence of 0.2% in children, is a complex condition characterized by stiff movement, muscle contractures, and abnormal gait that can diminish quality of life. Spastic CP accounts for approximately 83% of all CP cases and frequently co-occurs with other complex conditions, like epilepsy. An estimated 42% of spastic CP cases have co-occurring epilepsy. Unfortunately, CP is often difficult to diagnose. Although most children with CP are born with it or acquire it immediately after birth, many are not identified until after 19 months of age with CP diagnosis often not confirmed until 5 years of age. New bioinformatic approaches to identify CP earlier are needed. Recent studies indicate that altered DNA methylation patterns associated with CP may have diagnostic value. The potential confounding effects of co-occurrent epilepsy on these patterns are not known. We evaluated machine learning classification of CP patients with or without co-occurring epilepsy.
UNASSIGNED: Whole blood samples were collected from 30 study participants diagnosed with epilepsy (n=4), spastic CP (n=10), both (n=8), or neither (n=8). A novel Support-Vector-Machine learning algorithm was developed to identify methylation loci that have ability to classify CP from controls in the presence or absence of epilepsy. This algorithm was also employed to measure classification ability of identified methylation loci. After preprocessing of data, isolation of important methylation loci was performed in a binary comparison between CP and controls, as well as in a 4-way scheme, encapsulating epilepsy diagnoses. The classification ability was similarly assessed. CP Classification performance was evaluated with and without inclusion of epilepsy as a feature. Median F1 scores were 0.67 in 4-class comparison, and 1.0 in the binary classification, outperforming Linear-Discriminant-Analysis (0.57 and 0.86, respectively).
UNASSIGNED: This novel algorithm was able to classify study participants with spastic CP and/or epilepsy from controls with significant performance. The algorithm shows promise for rapid identification in methylation data of diagnostic methylation loci. In this model, Support Vector Machines outperformed Linear Discriminant Analysis in classification. In the evaluation of epigenetics-based diagnostics for CP, epilepsy may not be a significant confounding factor.

Biomolecules often exhibit complex free energy landscapes in which long-lived metastable states are separated by large energy barriers. Overcoming these barriers to robustly sample transitions between the metastable states with classical molecular dynamics (MD) simulations presents a challenge. To circumvent this issue, collective variable (CV)-based enhanced sampling MD approaches are often employed. Traditional CV selection relies on intuition and prior knowledge of the system. This approach introduces bias, which can lead to incomplete mechanistic insights. Thus, automated CV detection is desired to gain a deeper understanding of the system/process. Analysis of MD data with various machine-learning algorithms, such as principal component analysis (PCA), support vector machine, and linear discriminant analysis (LDA) based approaches have been implemented for automated CV detection. However, their performance has not been systematically evaluated on structurally and mechanistically complex biological systems. Here, we applied these methods to MD simulations of the MFSD2A (Major Facilitator Superfamily Domain 2A) lysolipid transporter in multiple functionally relevant metastable states with the goal of identifying optimal CVs that would structurally discriminate these states. Specific emphasis was on the automated detection and interpretive power of LDA-based CVs. We found that LDA methods, which included a novel gradient descent-based multiclass harmonic variant, termed GDHLDA, we developed here, outperform PCA in class separation, exhibiting remarkable consistency in extracting CVs critical for distinguishing metastable states. Furthermore, the identified CVs included features previously associated with conformational transitions in MFSD2A. Specifically, conformational shifts in transmembrane helix 7 and in residue Y294 on this helix emerged as critical features discriminating the metastable states in MFSD2A. This highlights the effectiveness of LDA-based approaches in automatically extracting from MD trajectories CVs of functional relevance that can be used to drive biased MD simulations to efficiently sample conformational transitions in the molecular system.

The potential of a voltametric E-tongue coupled with a custom data pre-processing stage to improve the performance of machine learning techniques for rapid discrimination of tomato purées between cultivars of different economic value has been investigated. To this aim, a sensor array with screen-printed carbon electrodes modified with gold nanoparticles (GNP), copper nanoparticles (CNP) and bulk gold subsequently modified with poly(3,4-ethylenedioxythiophene) (PEDOT), was developed to acquire data to be transformed by a custom pre-processing pipeline and then processed by a set of commonly used classifiers. The GNP and CNP-modified electrodes, selected based on their sensitivity to soluble monosaccharides, demonstrated good ability in discriminating samples of different cultivars. Among the different data analysis methods tested, Linear Discriminant Analysis (LDA) proved to be particularly suitable, obtaining an average F1 score of 99.26%. The pre-processing stage was beneficial in reducing the number of input features, decreasing the computational cost, i.e., the number of computing operations to be performed, of the entire method and aiding future cost-efficient hardware implementation. These findings proved that coupling the multi-sensing platform featuring properly modified sensors with the custom pre-processing method developed and LDA provided an optimal tradeoff between analytical problem solving and reliable chemical information, as well as accuracy and computational complexity. These results can be preliminary to the design of hardware solutions that could be embedded into low-cost portable devices.

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.
CONCLUSIONS: 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.

CONCLUSIONS: Hyperspectral features enable accurate classification of soybean seeds using linear discriminant analysis and GWAS for novel seed trait genes. Evaluating crop seed traits such as size, shape, and color is crucial for assessing seed quality and improving agricultural productivity. The introduction of the SUnSet toolbox, which employs hyperspectral sensor-derived image analysis, addresses this necessity. In a validation test involving 420 seed accessions from the Korean Soybean Core Collections, the pixel purity index algorithm identified seed- specific hyperspectral endmembers to facilitate segmentation. Various metrics extracted from ventral and lateral side images facilitated the categorization of seeds into three size groups and four shape groups. Additionally, quantitative RGB triplets representing seven seed coat colors, averaged reflectance spectra, and pigment indices were acquired. Machine learning models, trained on a dataset comprising 420 accession seeds and 199 predictors encompassing seed size, shape, and reflectance spectra, achieved accuracy rates of 95.8% for linear discriminant analysis model. Furthermore, a genome-wide association study utilizing hyperspectral features uncovered associations between seed traits and genes governing seed pigmentation and shapes. This comprehensive approach underscores the effectiveness of SUnSet in advancing precision agriculture through meticulous seed trait analysis.

Copper-cobalt bimetallic nitrogen-doped carbon-based nanoenzymatic materials (CuCo@NC) were synthesized using a one-step pyrolysis process. A three-channel colorimetric sensor array was constructed for the detection of seven antioxidants, including cysteine (Cys), uric acid (UA), tea polyphenols (TP), lysine (Lys), ascorbic acid (AA), glutathione (GSH), and dopamine (DA). CuCo@NC with peroxidase activity was used to catalyze the oxidation of TMB by H2O2 at three different ratios of metal sites. The ability of various antioxidants to reduce the oxidation products of TMB (ox TMB) varied, leading to distinct absorbance changes. Linear discriminant analysis (LDA) results showed that the sensor array was capable of detecting seven antioxidants in buffer and serum samples. It could successfully discriminate antioxidants with a minimum concentration of 10 nM. Thus, multifunctional sensor arrays based on CuCo@NC bimetallic nanoenzymes not only offer a promising strategy for identifying various antioxidants but also expand their applications in medical diagnostics and environmental analysis of food.

UNASSIGNED: Type 2 diabetes mellitus (T2DM) are 90% of diabetes cases, and its prevalence and incidence, including comorbidities, are rising worldwide. Clinically, diabetes and associated comorbidities are identified by biochemical and physical characteristics including glycemia, glycated hemoglobin (HbA1c), and tests for cardiovascular, eye and kidney disease.
UNASSIGNED: Diabetes may have a common etiology based on inflammation and oxidative stress that may provide additional information about disease progression and treatment options. Thus, identifying high-risk individuals can delay or prevent diabetes and its complications.
UNASSIGNED: In patients with or without hypertension and cardiovascular disease, as part of progression from no diabetes to T2DM, this research studied the changes in biomarkers between control and prediabetes, prediabetes to T2DM, and control to T2DM, and classified patients based on first-attendance data. Control patients and patients with hypertension, cardiovascular, and with both hypertension and cardiovascular diseases are 156, 148, 61, and 216, respectively.
UNASSIGNED: Linear discriminant analysis is used for classification method and feature importance, This study examined the relationship between Humanin and mitochondrial protein (MOTSc), mitochondrial peptides associated with oxidative stress, diabetes progression, and associated complications.
UNASSIGNED: MOTSc, reduced glutathione and glutathione disulfide ratio (GSH/GSSG), interleukin-1β (IL-1β), and 8-isoprostane were significant (P < .05) for the transition from prediabetes to t2dm, highlighting importance of mitochondrial involvement. complement component 5a (c5a) is a biomarker associated with disease progression and comorbidities, gsh gssg, monocyte chemoattractant protein-1 (mcp-1), 8-isoprostane being most important biomarkers.
UNASSIGNED: Comorbidities affect the hypothesized biomarkers as diabetes progresses. Mitochondrial oxidative stress indicators, coagulation, and inflammatory markers help assess diabetes disease development and provide appropriate medications. Future studies will examine longitudinal biomarker evolution.