Simultaneous determination of subspecies and geographic origins of 110 rice cultivars by microsatellite markers

Rice varieties of different subspecies types (indica rice and japonica rice) across various geographical origins (Hunan, Jiangsu, and Northeast China) were monitored using microsatellite markers (simple sequence repeats, SSR). 110 representative rice cultivars were collected from the main crop areas. Multiple methods including clustering analysis (neighbor-joining (NJ) method, unweighted pair-group method with arithmetic mean (UPGMA) method), principal component analysis (PCA) and model-based grouping were applied. The study revealed that 25 pairs of SSR markers exhibited a broad range of polymorphism information content (PIC) values, ranging from 0.240 to 0.830. Furthermore, our study successfully achieved a higher overall mean correct rate of 99.09% in determining the geographical origin of rice. Simultaneously, it accurately classified indica rice and japonica rice. These findings are significant as they provide an SSR fingerprint of 110 high-quality rice cultivars, serving as a valuable scientific resource for the detection of rice adulteration and traceability of its origin.

Detection of lung cancer through SERS analysis of serum

Early detection and postoperative assessment are crucial for improving overall survival among lung cancer patients. Here, we report a non-invasive technique that integrates Raman spectroscopy with machine learning for the detection of lung cancer. The study encompassed 88 postoperative lung cancer patients, 73 non-surgical lung cancer patients, and 68 healthy subjects. The primary aim was to explore variations in serum metabolism across these cohorts. Comparative analysis of average Raman spectra was conducted, while principal component analysis was employed for data visualization. Subsequently, the augmented dataset was used to train convolutional neural networks (CNN) and Resnet models, leading to the development of a diagnostic framework. The CNN model exhibited superior performance, as verified by the receiver operating characteristic curve. Notably, postoperative patients demonstrated an increased likelihood of recurrence, emphasizing the crucial need for continuous postoperative monitoring. In summary, the integration of Raman spectroscopy with CNN-based classification shows potential for early detection and postoperative assessment of lung cancer.

Rapid authenticity assessment of PGI Hongyuan yak milk based on SICRIT-QTOF MS

Hongyuan (HY) yaks live in a pollution-free environment, making HY yak milk a green food, but their short milk production period and low milk yield make yak milk precious and expensive. The phenomenon of counterfeiting HY yak milk with ordinary milk from other origins has already occurred, so the authenticity assessment of HY yak milk is necessary. This study developed a rapid soft ionisation by chemical reaction in transfer quadrupole time-of-flight mass spectrometry (SICRIT-QTOF MS) for HY yak milk differences assessment. Principal component analysis and orthogonal least squares discriminant analysis showed differences between HY milk and the other three origins. Twenty-eight differential compounds were screened out by variable importance in projection, fold change, P-value, and database matching. Furthermore, six characteristic compounds (proline, 2-hydroxy-3-methylbutyric acid, and l-isoleucine, etc.) of HY samples were putatively identified. The study demonstrated that SICRIT-QTOF MS has great potential for rapidly distinguishing the milk origin.

Low-abundance proteins-based label-free SERS approach for high precision detection of liver cancer with different stages

BACKGROUND

Surface-enhanced Raman scattering (SERS) technology have unique advantages of rapid, simple, and highly sensitive in the detection of serum, it can be used for the detection of liver cancer. However, some protein biomarkers in body fluids are often present at ultra-low concentrations and severely interfered with by the high-abundance proteins (HAPs), which will affect the detection of specificity and accuracy in cancer screening based on the SERS immunoassay. Clearly, there is a need for an unlabeled SERS method based on low abundance proteins, which is rapid, noninvasive, and capable of high precision detection and screening of liver cancer.

RESULTS

Serum samples were collected from 60 patients with liver cancer (27 patients with stage T1 and T2 liver cancer, 33 patients with stage T3 and T4 liver cancer) and 40 healthy volunteers. Herein, immunoglobulin and albumin were separated by immune sorption and Cohn ethanol fractionation. Then, the low abundance protein (LAPs) was enriched, and high-quality SERS spectral signals were detected and obtained. Finally, combined with the principal component analysis-linear discriminant analysis (PCA-LDA) algorithm, the SERS spectrum of early liver cancer (T1-T2) and advanced liver cancer (T3-T4) could be well distinguished from normal people, and the accuracy rate was 98.5% and 100%, respectively. Moreover, SERS technology based on serum LAPs extraction combined with the partial least square-support vector machine (PLS-SVM) successfully realized the classification and prediction of normal volunteers and liver cancer patients with different tumor (T) stages, and the diagnostic accuracy of PLS-SVM reached 87.5% in the unknown testing set.

SIGNIFICANCE

The experimental results show that the serum LAPs SERS detection combined with multivariate statistical algorithms can be used for effectively distinguishing liver cancer patients from healthy volunteers, and even achieved the screening of early liver cancer with high accuracy (T1 and T2 stage). These results showed that serum LAPs SERS detection combined with a multivariate statistical diagnostic algorithm has certain application potential in early cancer screening.

In situ rapid evaluation method of quality of peach kernels based on near infrared spectroscopy

This study aimed to perform a rapid in situ assessment of the quality of peach kernels using near infrared (NIR) spectroscopy, which included identifications of authenticity, species, and origins, and amygdalin quantitation. The in situ samples without any pretreatment were scanned by a portable MicroNIR spectrometer, while their powder samples were scanned by a benchtop Fourier transform NIR (FT-NIR) spectrometer. To improve the performance of the in situ determination model of the portable NIR spectrometer, the two spectrometers were first compared in identification and content models of peach kernels for both in situ and powder samples. Then, the in situ sample spectra were transferred by using the improved principal component analysis (IPCA) method to enhance the performance of the in situ model. After model transfer, the prediction performance of the in situ sample model was significantly improved, as shown by the correlation coefficient in the prediction set (Rp), root means square error of prediction (RMSEP), and residual prediction deviation (RPD) of the in situ model reached 0.9533, 0.0911, and 3.23, respectively, and correlation coefficient in the test set (Rt) and root means square error of test (RMSET) reached 0.9701 and 0.1619, respectively, suggesting that model transfer could be a viable solution to improve the model performance of portable spectrometers.

Diagnosis and classification of intestinal diseases with urine by surface-enhanced Raman spectroscopy

Intestinal Disease (ID) is often characterized by clinical symptoms such as malabsorption, intestinal dysfunction, and injury. If treatment is not timely, it will increase the risk of cancer. Early diagnosis of ID is the key to cure it. There are certain limitations of the conventional diagnostic methods, such as low sensitivity and specificity. Therefore, development of a highly sensitive, non-invasive diagnostic method for ID is extremely important. Urine samples are easier to collect and more sensitive to changes in biomolecules than other pathological diagnostic samples such as tissue and blood. In this paper, a diagnostic method of ID with urine by surface-enhanced Raman spectroscopy (SERS) is proposed. A classification model between ID patients and healthy controls (HC) and a classification model between different pathological types of ID (i.e., benign intestinal disease (BID) and colorectal cancer (CRC)) are established. Here, 830 urine samples, including 100 HC, 443 BID, and 287 CRC, were investigated by SERS. The ID/HC classification model was developed by analyzing the SERS spectra of 150 ID and 100 HC, while BID/CRC classification model was built with 300 BID and 150 CRC patients by principal component analysis (PCA)-support vector machines (SVM). The two established models were internally verified by leave-one-out-cross-validation (LOOCV). Finally, the BID/CRC classification model was further evaluated by 143 BID and 137 CRC patients as an external test set. It shows that the accuracy of the classification model validated by the LOOCV for ID/HC and BID/CRC is 86.4% and 85.56%, respectively. And the accuracy of the BID/CRC classification model with external test set is 82.14%. It shows that high accuracy can be achieved with these two established classification models. It indicates that ID patients in the general population can be identified and BID and CRC patients can be further classified with measuring urine by SERS. It shows that the proposed diagnostic method and established classification models provide valuable information for clinicians to early diagnose ID patients and analyze different stages of ID.

Principal component analysis-assisted zirconium-based metal-organic frameworks/DNA biosensor for the analysis of various phosphates

Considering the diversity of phosphates and their pivotal roles in physiological processes, detection of various phosphates related to their metabolism is urgent but challenging. Herein, we design a biosensor with zirconium-based MOFs (Zr-MOFs) and fluorophore-modified single-stranded DNA (F-ssDNA) for the analysis of phosphates. Relying on the interaction between Zr clusters and phosphate backbone, F-ssDNA is anchored on the surface of Zr-MOFs, inducing fluorescence resonance energy transfer (FRET) and subsequently quenching the fluorescence of F-ssDNA. Meanwhile, phosphates with different numbers of phosphate groups, molecular structures and coordination environments are able to adjust the FRET between Zr-MOFs and F-ssDNA via a site-occupying effect, recovering the fluorescence of F-ssDNA in distinct cases, which may result in diverse fluorescence signals. Consequently, seventeen phosphates and four phosphate mixtures are discriminated with the assistance of principal component analysis. These results provide new insight into the application of Zr-MOFs and broaden the path for the development of analytical methods for phosphates.

3D human ear modelling with parameterization technique and variation analysis

Anthropometry is vital to provide design references when seeking proper product fit. Nowadays, 3D anthropometry is widely used to provide more size and shape details for improving product designs. However, 3D ear anthropometry is still at an explorative stage, considering the complex ear morphology and other technical obstacles. The proposed research method in this study is applicable to analyse the 3D point cloud of the entire external ear. With the cross-parameterisation technique, the dataset was used to explore the morphological characteristics of the ear. Ear dimensions were automatically extracted and further analysed to explore the gender and symmetry differences using two-way ANOVA. The 3D ear models were investigated through Principal Component Analysis (PCA). The most significant variation was found in the helix and concha region, and the overall ear size is the second important factor determining ear variance. The statistical models were generated as 3D design references for ear-related products.Practitioner summary: This study revealed the morphological variations of the entire 3D external ear with a parameterised 3D ear dataset. Based on the PCA findings, a set of statistical models were generated as design references for product evaluation digitally or physically.

Surface-enhanced Raman spectroscopy of the filtrate portions of the blood serum samples of breast cancer patients obtained by using 30 kDa filtration device

Raman spectroscopy is reliable tool for analyzing and exploring early disease diagnosis related to body fluids, such as blood serum, which contain low molecular weight fraction (LMWF) and high molecular weight fraction (HMWF) proteins. The disease biomarkers consist of LMWF which are dominated by HMWF hence their analysis is difficult. In this study, in order to overcome this issue, centrifugal filter devices of 30 kDa were used to obtain filtrate and residue portions obtained from whole blood serum samples of control and breast cancer diagnosed patients. The filtrate portions obtained in this way are expected to contain the marker proteins of breast cancer of the size below this filter size. These may include prolactin, Microphage migration inhabitation factor (MIF), γ-Synuclein, BCSG1, Leptin, MUC1, RS/DJ-1 present in the centrifuged blood serum (filtrate portions) which are then analyzed by the SERS technique to recognize the SERS spectral characteristics associated with the progression of breast cancer in the samples of different stages as compared to the healthy ones. The key intention of this study is to achieve early-stage breast cancer diagnosis through the utilization of Surface Enhanced Raman Spectroscopy (SERS) after the centrifugation of healthy and breast cancer serum samples with Amicon ultra-filter devices of 30 kDa. The silver nanoparticles with high plasmon resonance are used as a substrate for SERS analysis. Principal Component Analysis (PCA) and Partial Least Discriminant Analysis (PLS-DA) models are utilized as spectral classification tools to assess and predict rapid, reliable, and non-destructive SERS-based analysis. Notably, they were particularly effective in distinguishing between different SERS spectral groups of the cancerous and non-cancerous samples. By comparing all these spectral data sets to each other PLSDA shows the 79 % accuracy, 76 % specificity, and 81 % sensitivity in samples with AUC value of AUC = 0.774 SERS has proven to be a valuable technique for the rapid identification of the SERS spectral features of blood serum and its filtrate fractions from both healthy individuals and those with breast cancer, aiding in disease diagnosis.

A nucleic acid-based surface-enhanced Raman scattering of gold nanorods in N-gene integrated principal component analysis for COVID-19 detection

A rapid, simple, sensitive, and selective point-of-care diagnosis tool kit is vital for detecting the coronavirus disease (COVID-19) based on the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) strain. Currently, the reverse transcriptase-polymerase chain reaction (RT-PCR) is the best technique to detect the disease. Although a good sensitivity has been observed in RT-PCR, the isolation and screening process for high sample volume is limited due to the time-consuming and laborious work. This study introduced a nucleic acid-based surface-enhanced Raman scattering (SERS) sensor to detect the nucleocapsid gene (N-gene) of SARS-CoV-2. The Raman scattering signal was amplified using gold nanoparticles (AuNPs) possessing a rod-like morphology to improve the SERS effect, which was approximately 12-15 nm in diameter and 40-50 nm in length. These nanoparticles were functionalised with the single-stranded deoxyribonucleic acid (ssDNA) complemented with the N-gene. Furthermore, the study demonstrates method selectivity by strategically testing the same virus genome at different locations. This focused approach showcases the method's capability to discern specific genetic variations, ensuring accuracy in viral detection. A multivariate statistical analysis technique was then applied to analyse the raw SERS spectra data using the principal component analysis (PCA). An acceptable variance amount was demonstrated by the overall variance (82.4 %) for PC1 and PC2, which exceeded the desired value of 80 %. These results successfully revealed the hidden information in the raw SERS spectra data. The outcome suggested a more significant thymine base detection than other nitrogenous bases at wavenumbers 613, 779, 1219, 1345, and 1382 cm-1. Adenine was also less observed at 734 cm-1, and ssDNA-RNA hybridisations were presented in the ketone with amino base SERS bands in 1746, 1815, 1871, and 1971 cm-1 of the fingerprint. Overall, the N-gene could be detected as low as 0.1 nM within 10 mins of incubation time. This approach could be developed as an alternative point-of-care diagnosis tool kit to detect and monitor the COVID-19 disease.

An optimized back propagation neural network on small samples spectral data to predict nitrite in water

Accurate detection of pollutant levels in water bodies using fusion algorithms combined with spectral data has become a critical issue for water conservation. However, the number of samples is too small and the model is unstable, which often leads to poor prediction and fails to achieve the measurement goal well. To address these challenges, this paper proposes a practical and effective method to precisely predict the concentrations of nitrite pollution in aquatic environments. The proposed method consists of three steps. Firstly, the dimension of the spectral data is reduced using Kernel Principal Component Analysis (KPCA), followed by sample augmentation using Generative Adversarial Network (GAN) to reduce calculation cost and increase the diversity and scale of the data. Secondly, several improvement strategies, including multi-cluster competitive and adaptive parameter updating, are introduced to enhance the capability of the Particle Swarm Optimization (PSO) algorithm. The improved PSO algorithm is then applied to optimize the initialization weights and biases of the Back Propagation neural network, thereby improving the model fitting and training performance. Finally, the developed prediction model is employed to predict the test set samples. The result suggests that the R2, RMSE, and MAE values are 0.976290, 0.008626, and 0.006617, which outperform the state-of-the-art and provided a promising model for the prediction of nitrite concentration in water.

Insights into early recovery from Long COVID-results from the German DigiHero Cohort

65 million people worldwide are estimated to suffer from long-term symptoms after their SARS-CoV-2 infection (Long COVID). However, there is still little information about the early recovery among those who initially developed Long COVID, i.e. had symptoms 4-12 weeks after infection but no symptoms after 12 weeks. We aimed to identify associated factors with this early recovery. We used data from SARS-CoV-2-infected individuals from the DigiHero study. Participants provided information about their SARS-CoV-2 infections and symptoms at the time of infection, 4-12 weeks, and more than 12 weeks post-infection. We performed multivariable logistic regression to identify factors associated with early recovery from Long COVID and principal component analysis (PCA) to identify groups among symptoms. 5098 participants reported symptoms at 4-12 weeks after their SARS-CoV-2 infection, of which 2441 (48%) reported no symptoms after 12 weeks. Men, younger participants, individuals with mild course of acute infection, individuals infected with the Omicron variant, and individuals who did not seek medical care in the 4-12 week period after infection had a higher chance of early recovery. In the PCA, we identified four distinct symptom groups. Our results indicate differential risk of continuing symptoms among individuals who developed Long COVID. The identified risk factors are similar to those for the development of Long COVID, so people with these characteristics are at higher risk not only for developing Long COVID, but also for longer persistence of symptoms. Those who sought medical help were also more likely to have persistent symptoms.

Integration of temporal & spatial properties of dynamic functional connectivity based on two-directional two-dimensional principal component analysis for disease analysis

Dynamic functional connectivity, derived from resting-state functional magnetic resonance imaging (rs-fMRI), has emerged as a crucial instrument for investigating and supporting the diagnosis of neurological disorders. However, prevalent features of dynamic functional connectivity predominantly capture either temporal or spatial properties, such as mean and global efficiency, neglecting the significant information embedded in the fusion of spatial and temporal attributes. In addition, dynamic functional connectivity suffers from the problem of temporal mismatch, i.e., the functional connectivity of different subjects at the same time point cannot be matched. To address these problems, this article introduces a novel feature extraction framework grounded in two-directional two-dimensional principal component analysis. This framework is designed to extract features that integrate both spatial and temporal properties of dynamic functional connectivity. Additionally, we propose to use Fourier transform to extract temporal-invariance properties contained in dynamic functional connectivity. Experimental findings underscore the superior performance of features extracted by this framework in classification experiments compared to features capturing individual properties.

Standardised Versioning of Datasets: a FAIR-compliant Proposal

This paper presents a standardised dataset versioning framework for improved reusability, recognition and data version tracking, facilitating comparisons and informed decision-making for data usability and workflow integration. The framework adopts a software engineering-like data versioning nomenclature ("major.minor.patch") and incorporates data schema principles to promote reproducibility and collaboration. To quantify changes in statistical properties over time, the concept of data drift metrics (d) is introduced. Three metrics (dP, dE,PCA, and dE,AE) based on unsupervised Machine Learning techniques (Principal Component Analysis and Autoencoders) are evaluated for dataset creation, update, and deletion. The optimal choice is the dE,PCA metric, combining PCA models with splines. It exhibits efficient computational time, with values below 50 for new dataset batches and values consistent with seasonal or trend variations. Major updates (i.e., values of 100) occur when scaling transformations are applied to over 30% of variables while efficiently handling information loss, yielding values close to 0. This metric achieved a favourable trade-off between interpretability, robustness against information loss, and computation time.

PLPCA: Persistent Laplacian-Enhanced PCA for Microarray Data Analysis

Over the years, Principal Component Analysis (PCA) has served as the baseline approach for dimensionality reduction in gene expression data analysis. Its primary objective is to identify a subset of disease-causing genes from a vast pool of thousands of genes. However, PCA possesses inherent limitations that hinder its interpretability, introduce class ambiguity, and fail to capture complex geometric structures in the data. Although these limitations have been partially addressed in the literature by incorporating various regularizers, such as graph Laplacian regularization, existing PCA based methods still face challenges related to multiscale analysis and capturing higher-order interactions in the data. To address these challenges, we propose a novel approach called Persistent Laplacian-enhanced Principal Component Analysis (PLPCA). PLPCA amalgamates the advantages of earlier regularized PCA methods with persistent spectral graph theory, specifically persistent Laplacians derived from algebraic topology. In contrast to graph Laplacians, persistent Laplacians enable multiscale analysis through filtration and can incorporate higher-order simplicial complexes to capture higher-order interactions in the data. We evaluate and validate the performance of PLPCA using ten benchmark microarray data sets that exhibit a wide range of dimensions and data imbalance ratios. Our extensive studies over these data sets demonstrate that PLPCA provides up to 12% improvement to the current state-of-the-art PCA models on five evaluation metrics for classification tasks after dimensionality reduction.

Preprocessing of Single Cell RNA Sequencing Data Using Correlated Clustering and Projection

Single-cell RNA sequencing (scRNA-seq) is widely used to reveal heterogeneity in cells, which has given us insights into cell-cell communication, cell differentiation, and differential gene expression. However, analyzing scRNA-seq data is a challenge due to sparsity and the large number of genes involved. Therefore, dimensionality reduction and feature selection are important for removing spurious signals and enhancing the downstream analysis. We present Correlated Clustering and Projection (CCP), a new data-domain dimensionality reduction method, for the first time. CCP projects each cluster of similar genes into a supergene defined as the accumulated pairwise nonlinear gene-gene correlations among all cells. Using 14 benchmark data sets, we demonstrate that CCP has significant advantages over classical principal component analysis (PCA) for clustering and/or classification problems with intrinsically high dimensionality. In addition, we introduce the Residue-Similarity index (RSI) as a novel metric for clustering and classification and the R-S plot as a new visualization tool. We show that the RSI correlates with accuracy without requiring the knowledge of the true labels. The R-S plot provides a unique alternative to the uniform manifold approximation and projection (UMAP) and t-distributed stochastic neighbor embedding (t-SNE) for data with a large number of cell types.

Recognition of breast cancer subtypes using FTIR hyperspectral data

Fourier-transform infrared spectroscopy (FTIR) is a powerful, non-destructive, highly sensitive and a promising analytical technique to provide spectrochemical signatures of biological samples, where markers like carbohydrates, proteins, and phosphate groups of DNA can be recognized in biological micro-environment. However, method of measurements of large cells need an excessive time to achieve high quality images, making its clinical use difficult due to speed of data-acquisition and lack of optimized computational procedures. To address such challenges, Machine Learning (ML) based technologies can assist to assess an accurate prognostication of breast cancer (BC) subtypes with high performance. Here, we applied FTIR spectroscopy to identify breast cancer subtypes in order to differentiate between luminal (BT474) and non-luminal (SKBR3) molecular subtypes. For this reason, we tested multivariate classification technique to extract feature information employing three-dimension (3D)-discriminant analysis approach based on 3D-principle component analysis-linear discriminant analysis (3D-PCA-LDA) and 3D-principal component analysis-quadratic discriminant analysis (3D-PCA-QDA), showing an improvement in sensitivity (98%), specificity (94%) and accuracy (98%) parameters compared to conventional unfolded methods. Our results evidence that 3D-PCA-LDA and 3D-PCA-QDA are potential tools for discriminant analysis of hyperspectral dataset to obtain superior classification assessment.

Deficits in force production during multifinger tasks demarcate cognitive dysfunction

BACKGROUND

The multifinger force deficit (MFFD) is the decline in force generated by each finger as the number of fingers contributing to an action is increased. It has been shown to associate with cognitive status.

AIMS

The aim was to establish whether a particularly challenging form of multifinger grip dynamometry, that provides minimal tactile feedback via cutaneous receptors and requires active compensation for reaction forces, will yield an MFFD that is more sensitive to cognitive status.

METHODS

Associations between measures of motor function, and cognitive status (Montreal Cognitive Assessment [MoCA]) and latent components of cognitive function (derived from 11 tests using principal component analysis), were estimated cross-sectionally using generalized partial rank correlations. The participants (n  =  62) were community dwelling, aged 65-87.

RESULTS

Approximately half the participants were unable to complete the dynamometry task successfully. Cognitive status demarcated individuals who could perform the task from those who could not. Among those who complied with the task requirements, the MFFD was negatively correlated with MoCA scores-those with the highest MoCA scores tended to exhibit the smallest deficits, and vice versa. There were corresponding associations with latent components of cognitive function.

DISCUSSION

The results support the view that neurodegenerative processes that are a feature of normal and pathological aging exert corresponding effects on expressions of motor coordination-in multifinger tasks, and cognitive sufficiency, due to their dependence on shared neural systems.

CONCLUSIONS

The outcomes add weight to the assertion that deficits in force production during multifinger tasks are sensitive to cognitive dysfunction.

Sensorimotor mechanisms selective to numerosity derived from individual differences

We have previously shown that after few seconds of adaptation by finger-tapping, the perceived numerosity of spatial arrays and temporal sequences of visual objects displayed near the tapping region is increased or decreased, implying the existence of a sensorimotor numerosity system (Anobile et al., 2016). To date, this mechanism has been evidenced only by adaptation. Here, we extend our finding by leveraging on a well-established covariance technique, used to unveil and characterize 'channels' for basic visual features such as colour, motion, contrast, and spatial frequency. Participants were required to press rapidly a key a specific number of times, without counting. We then correlated the precision of reproduction for various target number presses between participants. The results showed high positive correlations for nearby target numbers, scaling down with numerical distance, implying tuning selectivity. Factor analysis identified two factors, one for low and the other for higher numbers. Principal component analysis revealed two bell-shaped covariance channels, peaking at different numerical values. Two control experiments ruled out the role of non-numerical strategies based on tapping frequency and response duration. These results reinforce our previous reports based on adaptation, and further suggest the existence of at least two sensorimotor number channels responsible for translating symbolic numbers into action sequences.

FT-Raman spectra in combination with machine learning and multivariate analyses as a diagnostic tool in brain tumors

Brain tumors are one of the most dangerous, because the position of these are in the organ that governs all life processes. Moreover, a lot of brain tumor types were observed, but only one main diagnostic method was used - histopathology, for which preparation of sample was long. Consequently, a new, quicker diagnostic method is needed. In this paper, FT-Raman spectra of brain tissues were analyzed by Principal Component Analysis (PCA), Hierarchical Cluster Analysis (HCA), four different machine learning (ML) algorithms to show possibility of differentiating between glioblastoma G4 and meningiomas, as well as two different types of meningiomas (atypical and angiomatous). Obtained results showed that in meningiomas additional peak around 1503 cm-1 and higher level of amides was noticed in comparison with glioblastoma G4. In the case of meningiomas differentiation, in angiomatous meningiomas tissues lower level of lipids and polysaccharides were visible than in atypical meningiomas. Moreover, PCA analyses showed higher distinction between glioblastoma G4 and meningiomas in the FT-Raman range between 800 cm-1 and 1800 cm-1 and between two types of meningiomas in the range between 2700 cm-1 and 3000 cm-1. Decision trees showed, that the most important peaks to differentiate glioblastoma and meningiomas were at 1151 cm-1 and 2836 cm-1 while for angiomatous and atypical meningiomas - 1514 cm-1 and 2875 cm-1. Furthermore, the accuracy of obtained results for glioblastoma G4 and meningiomas was 88 %, while for meningiomas - 92 %. Consequently, obtained data showed possibility of using FT-Raman spectroscopy in diagnosis of different types of brain tumors.

Principal component analysis of a swine injury model identifies multiple phenotypes in trauma

BACKGROUND

Trauma is the third leading cause of death in the United States and the primary cause of death for people between the ages of 1 year and 44 years. In addition to tissue damage, trauma may also activate an inflammatory state known as trauma-induced coagulopathy (TIC) that is associated with clotting malfunctions, acidemia, and end-organ dysfunction. Prior work has also demonstrated benefit to acknowledging the type and severity of endothelial injury, coagulation derangements, and systemic inflammation in the management of trauma patients. This study builds upon prior work by combining laboratory, metabolic, and clinical metrics into an analysis of trauma phenotypes, evolution of phenotypes over time after trauma, and significance of trauma phenotype on mortality.

METHODS

Seventy 3-month-old female Yorkshire crossbred swine were randomized to injury and resuscitation groups. Principal component analysis (PCA) of longitudinal swine TEG data (Reaction time, Alpha-Angle, Maximum Amplitude, and Clot Lysis at 30 minutes), pH, lactate, and MAP was completed in R at baseline, 1 hour postinjury, 3 hours postinjury, 6 hours postinjury, and 12 hours postinjury. Subjects were compared by principal component factor scores to assess differences in survival, injury severity, and treatment group.

RESULTS

Among injured animals, three phenotypes were observed at each time point. Five phenotypes were associated with differences in survival, and of these, four were associated with differences in injury severity. Phenotype alignment was not significantly different by treatment group.

CONCLUSION

This application of PCA to a set of coagulation, hemodynamic, and organ perfusion variables has identified multiple evolving phenotypes after trauma. Some of these phenotypes may correlate with injury severity and may have implications for survival. Next steps include validating these findings over greater numbers of subjects and exploring other machine-learning techniques for phenotype identification.

LEVEL OF EVIDENCE

Level IV, Therapeutic/Care Management.

Decomposing the late positive potential to cannabis cues in regular cannabis users: A temporal-spatial principal component analysis

Cannabis use disorder (CUD) is increasing in the United States, yet, specific neural mechanisms of CUD are not well understood. Disordered substance use is characterized by heightened drug cue incentive salience, which can be measured using the late positive potential (LPP), an event-related potential (ERP) evoked by motivationally significant stimuli. The drug cue LPP is typically quantified by averaging the slow wave's scalp-recorded amplitude across its entire time course, which may obscure distinct underlying factors with differential predictive validity; however, no study to date has examined this possibility. In a sample of 105 cannabis users, temporo-spatial Principal Component Analysis was used to decompose cannabis cue modulation of the LPP into its underlying factors. Acute stress was also inducted to allow for identification of specific cannabis LPP factors sensitive to stress. Factor associations with CUD severity were also explored. Eight factors showed significantly increased amplitudes to cannabis images relative to neutral images. These factors spanned early (~372 ms), middle (~824 ms), and late (>1000 ms) windows across frontal, central, and parietal-occipital sites. CUD phenotype individual differences were primarily associated with frontal, middle/late latency factor amplitudes. Acute stress effects were limited to one early central and one late frontal factor. Taken together, results suggest that the cannabis LPP can be decomposed into distinct, temporal-spatial factors with differential responsivity to acute stress and CUD phenotype variability. Future individual difference studies examining drug cue modulation of the LPP should consider (1) frontalcentral poolings in addition to conventional central-parietal sites, and (2) later LPP time windows.

Deep reinforcement learning and convolutional autoencoders for anomaly detection of congenital inner ear malformations in clinical CT images

Detection of abnormalities within the inner ear is a challenging task even for experienced clinicians. In this study, we propose an automated method for automatic abnormality detection to provide support for the diagnosis and clinical management of various otological disorders. We propose a framework for inner ear abnormality detection based on deep reinforcement learning for landmark detection which is trained uniquely in normative data. In our approach, we derive two abnormality measurements: Dimage and Uimage. The first measurement, Dimage, is based on the variability of the predicted configuration of a well-defined set of landmarks in a subspace formed by the point distribution model of the location of those landmarks in normative data. We create this subspace using Procrustes shape alignment and Principal Component Analysis projection. The second measurement, Uimage, represents the degree of hesitation of the agents when approaching the final location of the landmarks and is based on the distribution of the predicted Q-values of the model for the last ten states. Finally, we unify these measurements in a combined anomaly measurement called Cimage. We compare our method's performance with a 3D convolutional autoencoder technique for abnormality detection using the patch-based mean squared error between the original and the generated image as a basis for classifying abnormal versus normal anatomies. We compare both approaches and show that our method, based on deep reinforcement learning, shows better detection performance for abnormal anatomies on both an artificial and a real clinical CT dataset of various inner ear malformations with an increase of 11.2% of the area under the ROC curve. Our method also shows more robustness against the heterogeneous quality of the images in our dataset.

Development and validation of an assessment index for quantifying cognitive task load in pilots under simulated flight conditions using heart rate variability and principal component analysis

To investigate whether high cognitive task load (CTL) for aircraft pilots can be identified by analysing heart-rate variability, electrocardiograms were recorded while cadet pilots (n = 68) performed the plane tracking, anti-gravity pedalling, and reaction tasks during simulated flight missions. Data for standard electrocardiogram parameters were extracted from the R-R-interval series. In the research phase, low frequency power (LF), high frequency power (HF), normalised HF, and LF/HF differed significantly between high and low CTL conditions (p < .05 for all). A principal component analysis identified three components contributing 90.62% of cumulative heart-rate variance. These principal components were incorporated into a composite index. Validation in a separate group of cadet pilots (n = 139) under similar conditions showed that the index value significantly increased with increasing CTL (p < .05). The heart-rate variability index can be used to objectively identify high CTL flight conditions.Practitioner summary: We used principal component analysis of electrocardiogram data to construct a composite index for identifying high cognitive task load in pilots during simulated flight. We validated the index in a separate group of pilots under similar conditions. The index can be used to improve cadet training and flight safety.Abbreviations: ANOVA: a one-way analysis of variance; AP: anti-gravity pedaling task; CTL: cognitive task load; ECG: electrocardiograms; HR: heart rate; HRV: heart-rate variability; HRVI: heart-rate variability index; PT: plane-tracking task; RMSSD: root-mean square of differences between consecutive R-R intervals; RT: reaction task; SDNN: standard deviation of R-R intervals; HF: high frequency power; HFnu: normalized HF; LF: low frequency power; LFnu: normalized LF; PCA: principal component analysis.

Production of chickpea protein hydrolysate at laboratory and pilot plant scales: Optimization using principal component analysis based on antioxidant activities

Chickpeas are a nutrient-rich source with optimal and high essential amino acid score. To evaluate its potential as a functional food ingredient, 36 chickpea protein hydrolysates were produced at the lab-scale using food-grade enzymes. Parameters including yields, protein content, hydrolysis degree, and antioxidant activities were employed to identify the most favourable conditions for scaling up production to a pilot plant level using a principal component analysis. The selected hydrolysate demonstrated commendable traits: a substantial content of essential amino acids and proteins at 67.71%, notable protein (73.12%) and weight (72.00%) yields, coupled with exceptional solubility exceeding 80%, and a noteworthy digestibility of 89.50%. Upon transition to pilot plant proportions, the hydrolysate retained its attenuated protein profile while exhibiting heightened antioxidant activities. Derived chickpea protein hydrolysates offer promise for innovative foods applications, impacting health and chronic disease prevention.

Oil in the Cookbook of Plasters: Differentiation of Lipid Binders in Wall Paintings Using Gas Chromatography-Mass Spectrometry and Principal Component Analysis

Wall paintings are integral to cultural heritage and offer rich insights into historical and religious beliefs. There exist various wall painting techniques that pose challenges in binder and pigment identification, especially in the case of egg/oil-based binders. GC-MS identification of lipidic binders relies routinely on parameters like the ratios of fatty acids within the plaster. However, the reliability of these ratios for binder identification is severely limited, as demonstrated in this manuscript. Therefore, a more reliable tool for effective differentiation between egg and oil binders based on a combination of diagnostic values, specific markers (cholesterol oxidation products), and PCA is presented in this study. Reference samples of wall paintings with egg and linseed oil binders with six different pigments were subjected to modern artificial ageing methods and subsequently analysed using two GC-MS instruments. A statistically significant difference (at a 95% confidence level) between the egg and oil binders and between the results from two GC-MS instruments was observed. These discrepancies between the results from the two GC-MS instruments are likely attributed to the heterogeneity of the samples with egg and oil binders. This study highlights the complexities in identifying wall painting binders and the need for innovative and revised analytical methods in conservation efforts.

Multivariate Analysis of Light-Activated SMOX Gas Sensors

Chemoresistive gas sensors made from SnO2, ZnO, WO3, and In2O3 have been prepared by flame spray pyrolysis. The sensors' response to CO and NO2 in darkness and under illumination at different wavelengths, using commercially available LEDs, was investigated. Operation at room temperature turned out to be impractical due to the condensation of water inside the porous sensing layers and the irreversible changes it caused. Accordingly, for sensors operated at 70 °C, a characterization procedure was developed and proven to deliver consistent data. The resulting data set was so complex that usual univariate data analysis was intricate and, consequently, was investigated by correlation and principal component analysis. The results show that light of different wavelengths affects not only the resistance of each material, both under exposure to the target gases in humidity and in its absence, but also the sensor response to humidity and the target gases. It was found that each of the materials behaves differently under light exposure, and it was possible to identify conditions that need further investigations.

Combined use of principal component analysis/multiple linear regression analysis and artificial neural network to assess the impact of meteorological parameters on fluctuation of selected PM2.5-bound elements

Based on the data of the State of Global Air (2020), air quality deterioration in Thailand has caused ~32,000 premature deaths, while the World Health Organization evaluated that air pollutants can decrease the life expectancy in the country by two years. PM2.5 was collected at three air quality observatory sites in Chiang-Mai, Bangkok, and Phuket, Thailand, from July 2020 to June 2021. The concentrations of 25 elements (Na, Mg, Al, Si, S, Cl, K, Ca, Sc, Ti, V, Cr, Mn, Fe, Co, Ni, Cu, Zn, Ga, As, Se, Br, Sr, Ba, and Pb) were quantitatively characterised using energy-dispersive X-ray fluorescence spectrometry. Potential adverse health impacts of some element exposures from inhaling PM2.5 were estimated by employing the hazard quotient and excess lifetime cancer risk. Higher cancer risks were detected in PM2.5 samples collected at the sampling site in Bangkok, indicating that vehicle exhaust adversely impacts human health. Principal component analysis suggests that traffic emissions, crustal inputs coupled with maritime aerosols, and construction dust were the three main potential sources of PM2.5. Artificial neural networks underlined agricultural waste burning and relative humidity as two major factors controlling the air quality of Thailand.

Predicting health outcomes with intensive longitudinal data collected by mobile health devices: a functional principal component regression approach

BACKGROUND

Intensive longitudinal data (ILD) collected in near real time by mobile health devices provide a new opportunity for monitoring chronic diseases, early disease risk prediction, and disease prevention in health research. Functional data analysis, specifically functional principal component analysis, has great potential to abstract trends in ILD but has not been used extensively in mobile health research.

OBJECTIVE

To introduce functional principal component analysis (fPCA) and demonstrate its potential applicability in estimating trends in ILD collected by mobile heath devices, assessing longitudinal association between ILD and health outcomes, and predicting health outcomes.

METHODS

fPCA and scalar-to-function regression models were reviewed. A case study was used to illustrate the process of abstracting trends in intensively self-measured blood glucose using functional principal component analysis and then predicting future HbA1c values in patients with type 2 diabetes using a scalar-to-function regression model.

RESULTS

Based on the scalar-to-function regression model results, there was a slightly increasing trend between daily blood glucose measures and HbA1c. 61% of variation in HbA1c could be predicted by the three preceding months' blood glucose values measured before breakfast (P < 0.0001, [Formula: see text]).

CONCLUSIONS

Functional data analysis, specifically fPCA, offers a unique tool to capture patterns in ILD collected by mobile health devices. It is particularly useful in assessing longitudinal dynamic association between repeated measures and outcomes, and can be easily integrated in prediction models to improve prediction precision.

Insight into the effect of cultivar and altitude on the identification of EnshiYulu tea grade in untargeted metabolomics analysis

The accurate identification of tea grade is crucial to the quality control of tea. However, existing methods lack sufficient generalization ability in identifying tea grades due to the effect of temporal and spatial factors. In this study, we analyzed the effect of cultivar and altitude on EnshiYulu (ESYL) tea grades and established a robust model to evaluate their quality. Principal component analysis (PCA) revealed that differences in variety and elevation can mask grade differences. Orthogonal projection to latent structure-discriminant analysis (OPLS-DA) was used for grade identification of samples from different altitudes. For ESYL tea samples above and below 800 m altitude, 75 and 35 grade differentiated metabolites were discovered, with 14 common differentiated metabolites. Based on reconstructed OPLS-DA models, the grades of multi-altitude sources ESYL were discriminated with a rate > 85%. These results demonstrate the potential of a grade discrimination model based on common differential metabolites, which exhibits generalization ability.

Fingerprinting based on spectral reflectance and chemometrics - An analytical approach aimed at combating the illegal trade of stingray meat in the Amazon

The survival of Amazon stingrays is threatened due to excessive fishing and habitat degradation. To address this issue, this study developed a groundbreaking method to authenticate and differentiate Amazon stingray meats using a portable spectrophotometer and chemometrics. Samples were collected from various species, including an endangered one with a commercialization ban and no population reduction records. Principal Component Analysis (PCA), identified natural groupings based on the meat's commercial origin, while Partial Least Squares-Discriminant Analysis (PLS-DA), accurately discriminated the commercial and geographic origins with 100 % accuracy. Moreover, Data-Driven Soft Independent Modeling of Class Analogy (DD-SIMCA), effectively distinguished Amazon stingray meat from other marketable species. This approach offers a rapid, precise, and non-destructive means for monitoring and controlling the illegal trade of these species, thereby supporting decision-making in the field and promoting the conservation and sustainability of freshwater stingrays in the Amazon region.

Application of surface-enhanced Raman spectroscopy to human serum for diagnosing liver cancer

This study investigates the application of surface-enhanced Raman spectroscopy (SERS) in the diagnosis of liver cancer using Ag@SiO2 nanoparticles as SERS substrates. A SERS test was conducted on serum samples obtained from patients with liver cancer and healthy individuals. After repeated several times experiments, it was found that the best SERS spectrum was obtained when the volume ratio of serum to deionized water was 1:2. Moreover, data preprocessing was performed on the tested SERS spectrum, and the preprocessed spectral data were combined with principal component analysis (PCA), partial least-squares discriminant analysis (PLS-DA), and orthogonal partial least squares discriminant analysis (OPLS-DA) for further analysis to classify the serum samples of patients with liver cancer and healthy individuals. The results showed that the classification effect of standard normal variate spectral data combined with the OPLS-DA was the best for the serum samples, with a classification accuracy of 97.98%, sensitivity of 97.14%, and specificity of 98.44%. Therefore, the SERS technology can be developed as a favorable method for the accurate diagnosis of liver cancer in the future.

Nasopharyngeal carcinoma cell screening based on the electroporation-SERS spectroscopy

Nasopharyngeal carcinoma (NPC) is a malignant tumor in head and neck. Early diagnosis can effectively improve the survival rate of patients. Nasopharyngeal exfoliative cytology, as a convenient and noninvasive auxiliary diagnostic method, is suitable for the population screening of NPC, but its diagnostic sensitivity is low. In this study, an electroporation-based SERS technique was proposed to detect and screen the clinical nasopharyngeal exfoliated cell samples. Firstly, nasopharyngeal swabs was used to collected the nasopharyngeal exfoliated cell samples from NPC patients (n = 54) and healthy volunteers (n = 60). Then, gold nanoparticles, as the Raman scattering enhancing substrates, were rapidly introduced into cells by electroporation technique for surface-enhanced Raman scattering (SERS) detection. Finally, SERS spectra combined with principal component analysis (PCA) and linear discriminant analysis (LDA) were employed to diagnose and distinguish NPC cell samples. Raman peak assignments combined with spectral differences reflected the biochemical changes associated with NPC, including nucleic acid, amino acid and carbohydrates. Based on the PCA-LDA approach, the sensitivity, specificity and accuracy of 98.15 %, 96.67 % and 97.37 %, respectively, were achieved for screening NPC. This study offers valuable assistance for noninvasive NPC auxiliary diagnosis, and has grate potential in expanding the application of the SERS technique in clinical cell sample testing.

Brain fiber structure estimation based on principal component analysis and RINLM filter

Diffusion magnetic resonance imaging is a technique for non-invasive detection of microstructure in the white matter of the human brain, which is widely used in neuroscience research of the brain. However, diffusion-weighted images(DWI) are sensitive to noise, which affects the subsequent reconstruction of fiber orientation direction, microstructural parameter estimation and fiber tracking. In order to better eliminate the noise in diffusion-weighted images, this study proposes a noise reduction method combining Marchenko-Pastur principal component analysis(MPPCA) and rotation-invariant non-local means filter(RINLM) to further remove residual noise and preserve the image texture detail information. In this study, the algorithm is applied to the fiber structure and the prevailing microstructural models within the human brain voxels based on simulated and real human brain datasets. Experimental comparisons between the proposed method and the state-of-the-art methods are performed in single-fiber, multi-fiber, crossed and curved-fiber regions as well as in different microstructure estimation models. Results demonstrated the superior performance of the proposed method in denoising DWI data, which can reduce the angular error in fiber orientation reconstruction to obtain more valid fiber structure estimation and enable more complete fiber tracking trajectories with higher coverage. Meanwhile, the method reduces the estimation errors of various white matter microstructural parameters and verifies the performance of the method in white matter microstructure estimation.

Identification of Commercial Antimalarial Herbal Drugs Using Laser-Induced Autofluorescence Technique and Multivariate Algorithms

In malaria-prone developing countries the integrity of Anti-Malarial Herbal Drugs (AMHDs) which are easily preferred for treatment can be compromised. Currently, existing techniques for identifying AMHDs are destructive. We report on the use of non-destructive and sensitive technique, Laser-Induced-Autofluorescence (LIAF) in combination with multivariate algorithms for identification of AMHDs. The LIAF spectra were recorded from commercially prepared decoction AMHDs purchased from accredited pharmacy shop in Ghana. Deconvolution of the LIAF spectra revealed secondary metabolites belonging to derivatives of alkaloids and classes of phenolic compounds of the AMHDs. Principal Component Analysis (PCA) and Hierarchical Clustering Analysis (HCA) were able to discriminate the AMHDs base on their physicochemical properties. Based on two principal components, the PCA- QDA (Quadratic Discriminant Analysis), PCA-LDA (Linear Discriminant Analysis), PCA-SVM (Support Vector Machine) and PCA-KNN (K-Nearest Neighbour) models were developed with an accuracy performance of 99.0, 99.7, 100.0, and 100%, respectively, in identifying AMHDs. PCA-SVM and PCA-KNN provided the best classification and stability performance. The LIAF technique in combination with multivariate techniques may offer a non-destructive and viable tool for AMHDs identification.

Botanical origin and elemental content of Turkish honey: Implications for health risks from essential and non-essential elements

Honey, which is popular for its taste and health benefits, can pose health risks due to excessive levels of essential and non-essential elements. Turkey's unique geographical location and biodiversity have made it a major player in the global honey industry. This study analysed Turkish honey samples to determine their botanical origin and elemental content, and to assess non-carcinogenic risks associated with their consumption. Twelve samples were classified as monofloral, while the rest were considered multifloral. The results showed that the levels of elements in the honey samples varied significantly depending on the plant source and geographical location (p < 0.05). However, the health risk assessment for both adults and children indicated that the levels of these elements do not pose a health risk. Principal component -analysis has revealed a correlation among the elements present in honey samples. Overall, the risk of exposure to toxic elements in honey is low unless consumed excessively.

K-t PCA accelerated in-plane balanced steady-state free precession phase-contrast (PC-SSFP) for all-in-one diastolic function evaluation

PURPOSE

Diastolic function evaluation requires estimates of early and late diastolic mitral filling velocities (E and A) and of mitral annulus tissue velocity (e'). We aimed to develop an MRI method for simultaneous all-in-one diastolic function evaluation in a single scan by generating a 2D phase-contrast (PC) sequence with balanced steady-state free precession (bSSFP) contrast (PC-SSFP). E and A could then be measured with PC, and e' estimated by valve tracking on the magnitude images, using an established deep learning framework.

METHODS

Our PC-SSFP used in-plane flow-encoding, with zeroth and first moment nulling over each TR. For further acceleration, different k-t principal component analysis (PCA) methods were investigated with both retrospective and prospective undersampling. PC-SSFP was compared to separate balanced SSFP cine and PC-gradient echo acquisitions in phantoms and in 10 healthy subjects.

RESULTS

Phantom experiments showed that PC-SSFP measured accurate velocities compared to PC-gradient echo (r = 0.98 for a range of pixel-wise velocities -80 cm/s to 80 cm/s). In subjects, PC-SSFP generated high SNR and myocardium-blood contrast, and excellent agreement for E (limits of agreement [LOA] 0.8 ± 2.4 cm/s, r = 0.98), A (LOA 2.5 ± 4.1 cm/s, r = 0.97), and e' (LOA 0.3 ± 2.6 cm/s, r = 1.00), versus the standard methods. The best k-t PCA approach processed the complex difference data and substituted in raw k-space data. With prospective k-t PCA acceleration, higher frame rates were achieved (50 vs. 25 frames per second without k-t PCA), yielding a 13% higher e'.

CONCLUSION

The proposed PC-SSFP method achieved all-in-one diastolic function evaluation.

A critical assessment of sparse PCA (research): why (one should acknowledge that) weights are not loadings

Principal component analysis (PCA) is an important tool for analyzing large collections of variables. It functions both as a pre-processing tool to summarize many variables into components and as a method to reveal structure in data. Different coefficients play a central role in these two uses. One focuses on the weights when the goal is summarization, while one inspects the loadings if the goal is to reveal structure. It is well known that the solutions to the two approaches can be found by singular value decomposition; weights, loadings, and right singular vectors are mathematically equivalent. What is often overlooked, is that they are no longer equivalent in the setting of sparse PCA methods which induce zeros either in the weights or the loadings. The lack of awareness for this difference has led to questionable research practices in sparse PCA. First, in simulation studies data is generated mostly based only on structures with sparse singular vectors or sparse loadings, neglecting the structure with sparse weights. Second, reported results represent local optima as the iterative routines are often initiated with the right singular vectors. In this paper we critically re-assess sparse PCA methods by also including data generating schemes characterized by sparse weights and different initialization strategies. The results show that relying on commonly used data generating models can lead to over-optimistic conclusions. They also highlight the impact of choice between sparse weights versus sparse loadings methods and the initialization strategies. The practical consequences of this choice are illustrated with empirical datasets.

Genomic background of biotypes related to growth, carcass and meat quality traits in Duroc pigs based on principal component analysis

As the swine industry continues to explore pork quality traits alongside growth, feed efficiency and carcass leanness traits, it becomes imperative to understand their underlying genetic relationships. Due to this increase in the number of desirable traits, animal breeders must also consider methods to efficiently perform direct genetic changes for each trait and evaluate alternative selection indexes with different sets of phenotypic measurements. Principal component analysis (PCA) and genome-wide association studies (GWAS) can be combined to understand the genetic architecture and biological mechanisms by defining biological types (biotypes) that relate these valuable traits. Therefore, the main objectives of this study were to: (1) estimate genomic-based genetic parameters; (2) define animal biotypes utilizing PCA; and (3) utilize GWAS to link the biotypes to candidate genes and quantitative trait loci (QTL). The phenotypic dataset included 2583 phenotypic records from female Duroc pigs from a terminal sire line. The pedigree file contained 193,764 animals and the genotype file included 21,309 animals with 35,651 single nucleotide polymorphisms (SNPs). Eight principal components (PCs), accounting for a total of 99.7% of the population variation, were defined for three growth, eight conventional carcass, 10 pork quality and 18 novel carcass traits. The eight biotypes defined from the PCs were found to be related to growth rate, maturity, meat quality and body structure, which were then related to candidate genes. Of the 175 candidate genes found, six of them [LDHA (SSC1), PIK3C3 (SSC6), PRKAG3 (SSC15), VRTN (SSC7), DLST (SSC7) and PAPPA (SSC1)] related to four PCs were found to be associated with previously defined QTL, linking the biotypes with biological processes involved with muscle growth, fat deposition, glycogen levels and skeletal development. Further functional analyses helped to make connections between biotypes, relating them through common KEGG pathways and gene ontology (GO) terms. These findings contribute to a better understanding of the genetic relationships between growth, carcass and meat quality traits in Duroc pigs, enabling breeders to better understand the biological mechanisms underlying the phenotypic expression of these traits.

Fast screening using attenuated total reflectance- fourier transform infrared (ATR-FTIR) spectroscopy of patients based on D-dimer threshold value

Attenuated Total Reflectance-Fourier transform infrared (ATR-FTIR) spectroscopy is an emerging technology in the medical field. Blood D-dimer was initially studied as a marker of the activation of coagulation and fibrinolysis. It is mainly used as a potential diagnosis screening test for pulmonary embolism or deep vein thrombosis but was recently associated with COVID-19 severity. This study aimed to evaluate the use of ATR-FTIR spectroscopy with machine learning to classify plasma D-dimer concentrations. The plasma ATR-FTIR spectra from 100 patients were studied through principal component analysis (PCA) and two supervised approaches: genetic algorithm with linear discriminant analysis (GA-LDA) and partial least squares with linear discriminant (PLS-DA). The spectra were truncated to the fingerprint region (1800-1000 cm-1). The GA-LDA method effectively classified patients according to D-dimer cutoff (≤0.5 μg/mL and >0.5 μg/mL) with 87.5 % specificity and 100 % sensitivity on the training set, and 85.7 % specificity, and 95.6 % sensitivity on the test set. Thus, we demonstrate that ATR-FTIR spectroscopy might be an important additional tool for classifying patients according to D-dimer values. ATR-FTIR spectral analyses associated with clinical evidence can contribute to a faster and more accurate medical diagnosis, reduce patient morbidity, and save resources and demand for professionals.

Glioblastoma Pseudoprogression Discrimination Using Multiparametric Magnetic Resonance Imaging, Principal Component Analysis, and Supervised and Unsupervised Machine Learning

BACKGROUND

One of the most frequent phenomena in the follow-up of glioblastoma is pseudoprogression, present in up to half of cases. The clinical usefulness of discriminating this phenomenon through magnetic resonance imaging and nuclear medicine has not yet been standardized; in this study, we used machine learning on multiparametric magnetic resonance imaging to explore discriminators of this phenomenon.

METHODS

For the study, 30 patients diagnosed with IDH wild-type glioblastoma operated on at both study centers in 2011-2020 were selected; 15 patients corresponded to early tumor progression and 15 patients to pseudoprogression. Using unsupervised learning, the number of clusters and tumor segmentation was recorded using gap-stat and k-means method, adjusting to voxel adjacency. In a second phase, a class prediction was carried out with a multinomial logistic regression supervised learning method; the outcome variables were the percentage of assignment, class overrepresentation, and degree of voxel adjacency.

RESULTS

Unsupervised learning of the tumor in its diagnosis shows up to 14 well-differentiated tumor areas. In the supervised learning phase, there is a higher percentage of assigned classes (P < 0.01), less overrepresentation of classes (P < 0.01), and greater adjacency (55% vs. 33%) in cases of true tumor progression compared with pseudoprogression.

CONCLUSIONS

True tumor progression preserves the multidimensional characteristics of the basal tumor at the voxel and region of interest level, resulting in a characteristic differential pattern when supervised learning is used.

Characterization of biomarkers in blood serum for cancer diagnosis in dogs using Raman spectroscopy

Biomarkers of cancer in sera of domestic dogs were detected through Raman spectroscopy with 830 nm excitation. Raman spectra of sera from 61 dogs (31 healthy and 30 with cancer, resulting in 154 and 200 spectra, respectively) were submitted to principal component analysis (PCA) for feature extraction and partial least squares (PLS) regression for discrimination between Healthy and Cancer groups. In the PCA, the peaks at 1132, 1342, 1368, and 1453 cm-1 (albumin and phenylalanine) were higher for the Cancer group. The "redshift" of the peaks at 621, 1003, and 1032 cm-1 (conformational change in proteins and/or bonds at sites close to the aromatic ring of amino acids) occurred in the Cancer group, and the peaks at 451 cm-1 (tryptophan) and 1441 cm-1 (lipids) were higher for the Healthy group. The PLS-DA classified the serum spectra in Healthy and Cancer groups with high accuracy (78%).

Financing sources, green investment, and environmental performance: Cross-country evidence

This article investigates the influence of financing sources and financial constraints on green investment, based on a study conducted with a sample of Eastern European SMEs from 2018 to 2020. We constructed a green investment proxy using principal component analysis, revealing two principal pillars: pure green investment and mixed green investment. Employing two-stage least squares regression analysis (2SLS) and instrumental probit (IV Probit), our results demonstrate that internal finance positively impacts green investment. Conversely, we find that leverage and financial constraints negatively correlate with green investment and environmental performance. The findings of this study provide compelling evidence that SMEs operating in the Eastern European region face significant financial constraints, impeding their ability to adopt responsible investments aimed at reducing their considerable environmental footprints. These results hold valuable implications for both managers and policymakers, emphasizing the importance of facilitating increased access to debt and devising green financial incentives to promote environmentally responsible investments among Eastern European SMEs, particularly during periods of conflicts.

Assessing the quality of constructed technosols enabled holistic monitoring of ecological restoration

The soil quality index (SQI) serves as a general ecological restoration indicator, however, statistics approaches that accurately assess the minimum data set (MDS) for SQI remain susceptible. The present study aims to evaluate the short-term reclamation results at the Ferro-Carvão stream and propose a system for ecological restoration monitoring, by selecting influential attributes and indexing soil quality. We hypothesized that the reclamation activities at the Ferro-Carvão stream, referred to as the "Marco zero" (MZ) area, can bring its soil quality to levels comparable to those of the native area. We collected soil samples at 0-20 and 20-40 cm depths from transects of MZ and reference sites (R1 and R2). Principal component analysis showed the MDS for each soil depth. Permutational analysis of variance, in conjunction with Nonmetric Multidimensional Scaling, exposed relationships between transects of areas. An additive non-linear factorial algorithm allowed SQI assessment. The results indicated a similar soil quality between transects of areas at 0-20 cm depth, whereas a dissimilarity at 20-40 cm. To sum up, reclamation activities allowed MZ-constructed Technosol to present a soil quality similar to native areas. The soil quality assessment at both depths offered insights into reclamation activities' immediate and long-term impacts on the Ferro-Carvão stream. This robust framework effectively monitors ecological restoration progress and guides future efforts in post-mining and post-dam collapse sites.

Extrapolation study for determining the time since injury in a rat subcutaneous hematoma model utilizing ATR-FTIR spectroscopy

The determination of the time of an injury has been a major problem in forensic science due to the lack of objective, reliable and portable methods. In this study, a subcutaneous hemorrhage model in rats was established over six days, and attenuated total reflection-Fourier transform infrared (ATR-FTIR) spectroscopy coupled with chemometrics was used to determine the time since injury. Initial principal component analysis (PCA) showed variance among hematoma sites. Subsequently, spectral data were acquired to establish a dependable partial least square (PLS) regression model with predictive abilities. The root mean square error of cross-validation (RMSECV) and the root mean square error of prediction (RMSEP) values produced by a genetic algorithm (GA) were 0.64 d (R2 = 0.88) and 0.57 d (R2 = 0.90), respectively. Few variables were involved in the model, and significantly better results were obtained in comparison to the conventional full-spectrum PLS model. In combination with the results of variable importance in projection (VIP) scores, all components, including proteins, nucleic acids and phospholipids, provided inferences regarding the samples at different time points; additionally, amide I and II bands represented the secondary structure of proteins and provided the largest contribution. Based on our preliminary study, the combination of swift and nondamaging ATR-FTIR spectroscopy with chemometrics could prove to be an advantageous approach for gauging the age of an injury in the forensic field.

Raman spectroscopy combined with multivariate statistical algorithms for the simultaneous screening of cervical and breast cancers

Breast and cervical cancers are becoming the leading causes of death among women worldwide, but current diagnostic methods have many drawbacks, such as being time-consuming and high cost. Raman spectroscopy, as a rapid, reliable, and non-destructive spectroscopic detection technique, has achieved many breakthrough results in the screening and prognosis of various cancer tumors. Therefore, in this study, Raman spectroscopy technology was used to diagnose breast cancer and cervical cancer. A total of 225 spectra were recorded from 87 patients with cervical cancer, 60 patients with breast cancer, and 78 healthy individuals. The obvious difference in Raman spectrum between the three groups was mainly shown at 809 cm-1 (tyrosine), 958 cm-1 (carotenoid), 1004 cm-1 (phenylalanine), 1154 cm-1 (β-carotene), 1267 cm-1 (Amide III), 1445 cm-1 (phospholipids), 1515 cm-1 (β-carotene), and 1585 cm-1 (C = C olefinic stretch). We used one-way analysis of variance for these peaks and demonstrated that they were significantly different. Then, we combined the detected Raman spectra with multivariate statistical calculations using the principal component analysis-linear discrimination algorithm (PCA-LDA) to discriminate between the three groups of collected serum samples. The diagnostic results showed that the model's accuracy, precision, recall, and F1 score of the model were 92.90%, 92.62%, 92.10%, and 92.36%, respectively. These results suggest that Raman spectroscopy can achieve ultra-sensitive detection of serum, and the developed diagnostic models have great potential for the prognosis and simultaneous screening of cervical and breast cancers.

Dimension reduction and outlier detection of 3-D shapes derived from multi-organ CT images

BACKGROUND

Unsupervised clustering and outlier detection are important in medical research to understand the distributional composition of a collective of patients. A number of clustering methods exist, also for high-dimensional data after dimension reduction. Clustering and outlier detection may, however, become less robust or contradictory if multiple high-dimensional data sets per patient exist. Such a scenario is given when the focus is on 3-D data of multiple organs per patient, and a high-dimensional feature matrix per organ is extracted.

METHODS

We use principal component analysis (PCA), t-distributed stochastic neighbor embedding (t-SNE) and multiple co-inertia analysis (MCIA) combined with bagplots to study the distribution of multi-organ 3-D data taken by computed tomography scans. After point-set registration of multiple organs from two public data sets, multiple hundred shape features are extracted per organ. While PCA and t-SNE can only be applied to each organ individually, MCIA can project the data of all organs into the same low-dimensional space.

RESULTS

MCIA is the only approach, here, with which data of all organs can be projected into the same low-dimensional space. We studied how frequently (i.e., by how many organs) a patient was classified to belong to the inner or outer 50% of the population, or as an outlier. Outliers could only be detected with MCIA and PCA. MCIA and t-SNE were more robust in judging the distributional location of a patient in contrast to PCA.

CONCLUSIONS

MCIA is more appropriate and robust in judging the distributional location of a patient in the case of multiple high-dimensional data sets per patient. It is still recommendable to apply PCA or t-SNE in parallel to MCIA to study the location of individual organs.

A novel approach using ATR-FTIR spectroscopy and chemometric analysis to distinguish male and female human hair samples

This article presents an attempt to discriminate between human male and female hair samples using a single strand of scalp hair. The methodology involves the non-destructive application of ATR-FTIR spectroscopy coupled with chemometric analysis. A total of 96 hair samples, evenly distributed between 48 male and 48 female volunteers from India, were collected. Spectral analysis revealed subtle differences between the two groups, and reliance on visual interpretation might introduce biasness. To avoid subjective biases, chemometric techniques such as principal component analysis (PCA) and partial least square-discriminant analysis (PLS-DA) were employed for enhanced data visualization and separation. PCA results revealed that the first 10 principal components accounted for 93% of the total variance, with three significant PCs. The PLS-DA model demonstrated a remarkable sensitivity and specificity in sex discrimination from hair samples, establishing its efficacy as a robust classification tool. Furthermore, the proposed model exhibited 100% accuracy in predicting unknown samples, underscoring its potential applicability in real-world scenarios. These outcomes affirm the viability of our approach for non-invasive classification of human male and female hair based on single-strand scalp hair analysis.

An outlier removal method based on PCA-DBSCAN for blood-SERS data analysis

Surface-enhanced Raman spectroscopy (SERS) has shown promising potential in cancer screening. In practical applications, Raman spectra are often affected by deviations from the spectrometer, changes in measurement environments, and anomalies in spectrum characteristic peak intensities due to improper sample storage. Previous research has overlooked the presence of outliers in categorical data, leading to significant impacts on model learning outcomes. In this study, we propose a novel method, called Principal Component Analysis and Density Based Spatial Clustering of Applications with Noise (PCA-DBSCAN) to effectively remove outliers. This method employs dimensionality reduction and spectral data clustering to identify and remove outliers. The PCA-DBSCAN method introduces adjustable parameters (Eps and MinPts) to control the clustering effect. The effectiveness of the proposed PCA-DBSCAN method is verified through modeling on outlier-removed datasets. Further refinement of the machine learning model and PCA-DBSCAN parameters resulted in the best cancer screening model, achieving 97.41% macro-average recall and 97.74% macro-average F1-score. This paper introduces a new outlier removal method that significantly improves the performance of the SERS cancer screening model. Moreover, the proposed method serves as inspiration for outlier detection in other fields, such as biomedical research, environmental monitoring, manufacturing, quality control, and hazard prediction.

HyperVein: A Hyperspectral Image Dataset for Human Vein Detection

HyperSpectral Imaging (HSI) plays a pivotal role in various fields, including medical diagnostics, where precise human vein detection is crucial. HyperSpectral (HS) image data are very large and can cause computational complexities. Dimensionality reduction techniques are often employed to streamline HS image data processing. This paper presents a HS image dataset encompassing left- and right-hand images captured from 100 subjects with varying skin tones. The dataset was annotated using anatomical data to represent vein and non-vein areas within the images. This dataset is utilised to explore the effectiveness of dimensionality reduction techniques, namely: Principal Component Analysis (PCA), Folded PCA (FPCA), and Ward's Linkage Strategy using Mutual Information (WaLuMI) for vein detection. To generate experimental results, the HS image dataset was divided into train and test datasets. Optimum performing parameters for each of the dimensionality reduction techniques in conjunction with the Support Vector Machine (SVM) binary classification were determined using the Training dataset. The performance of the three dimensionality reduction-based vein detection methods was then assessed and compared using the test image dataset. Results show that the FPCA-based method outperforms the other two methods in terms of accuracy. For visualization purposes, the classification prediction image for each technique is post-processed using morphological operators, and results show the significant potential of HS imaging in vein detection.