Advancing prostate cancer detection: a comparative analysis of PCLDA-SVM and PCLDA-KNN classifiers for enhanced diagnostic accuracy.

This investigation aimed to assess the effectiveness of different classification models in diagnosing prostate cancer using a screening dataset obtained from the National Cancer Institute's Cancer Data Access System. The dataset was first reduced using the PCLDA method, which combines Principal Component Analysis and Linear Discriminant Analysis. Two classifiers, Support Vector Machine (SVM) and k-Nearest Neighbour (KNN), were then applied to compare their performance. The results showed that the PCLDA-SVM model achieved an impressive accuracy rate of 97.99%, with a precision of 0.92, sensitivity of 92.83%, specificity of 97.65%, and F1 score of 0.93. Additionally, it demonstrated a low error rate of 0.016 and a Matthews Correlation Coefficient (MCC) and Kappa coefficient of 0.946. On the other hand, the PCLDA-KNN model also performed well, achieving an accuracy of 97.8%, precision of 0.93, sensitivity of 93.39%, specificity of 97.86%, an F1 score of 0.92, a high MCC and Kappa coefficient of 0.98, and an error rate of 0.006. In conclusion, the PCLDA-SVM method exhibited improved efficacy in diagnosing prostate cancer compared to the PCLDA-KNN model. Both models, however, showed promising results, suggesting the potential of these classifiers in prostate cancer diagnosis.

Enhanced Multiscale Principal Component Analysis for Improved Sensor Fault Detection and Isolation.

Multiscale PCA (MSPCA) is a well-established fault-detection and isolation (FDI) technique. It utilizes wavelet analysis and PCA to extract important features from process data. This study demonstrates limitations in the conventional MSPCA fault detection algorithm, thereby proposing an enhanced MSPCA (EMSPCA) FDI algorithm that uses a new wavelet thresholding criterion. As such, it improves the projection of faults in the residual space and the threshold estimation of the fault detection statistic. When tested with a synthetic model, EMSPCA resulted in a 30% improvement in detection rate with equal false alarm rates. The EMSPCA algorithm also relies on the novel application of reconstruction-based fault isolation at multiple scales. The proposed algorithm reduces fault smearing and consequently improves fault isolation performance. The paper will further investigate the use of soft vs. hard wavelet thresholding, decimated vs. undecimated wavelet transforms, the choice of wavelet decomposition depth, and their implications on FDI performance.The FDI performance of the developed EMSPCA method was illustrated for sensor faults. This undertaking considered synthetic data, the simulated data of a continuously stirred reactor (CSTR), and experimental data from a packed-bed pilot plant. The results of these examples show the advantages of EMSPCA over existing techniques.

PCA driven mixed filter pruning for efficient convNets.

Deployment of the deep neural networks (DNNs) on resource-constrained devices is a challenging task due to their limited memory and computational power. In most cases, the pruning techniques do not prune the DNNs to full extent and redundancy still exists in these models. Considering this, a mixed filter pruning approach based on principal component analysis (PCA) and geometric median is presented. First, a pre-trained model is analyzed by using PCA to identify the important filters for every layer. These important filters are then used to reconstruct the network with a fewer number of layers and a fewer number of filters per layer. A new network with optimized structure is constructed and trained on the data. Secondly, the trained model is then analyzed using geometric median as a base. The redundant filters are identified and removed which results in further compression of the network. Finally, the pruned model is fine tuned to regain the accuracy. Experiments on CIFAR-10, CIFAR-100 and ILSVRC 2017 datasets show that the proposed mixed pruning approach is feasible and can compress the network to greater extent without any significant loss to accuracy. With VGG-16 on CIFAR-10, the number of operations and parameters are reduced to 18.56× and 3.33×, respectively, with almost 1% loss in the accuracy. The compression rate for AlexNet on CIFAR-10 dataset is 2.61× and 4.85× in terms of number of operations and number of parameters, respectively, with a gain of 1.2% in the accuracy. On CIFAR-100, VGG-19 is compressed by 16.02 X in terms of number of operations and 36× in terms of number of parameters with a 2.6% loss of accuracy. Similarly, the compression rate for VGG-19 network on ILSVRC 2017 dataset is 1.87× and 2.4× for operations and parameters with 0.5% loss in accuracy.

A comparative analysis of the principal component analysis and entropy weight methods to establish the indexing measurement.

BACKGROUND: As the world's largest coal producer, China was accounted for about 46% of global coal production. Among present coal mining risks, methane gas (called gas in this paper) explosion or ignition in an underground mine remains ever-present. Although many techniques have been used, gas accidents associated with the complex elements of underground gassy mines need more robust monitoring or warning systems to identify risks. This paper aimed to determine which single method between the PCA and Entropy methods better establishes a responsive weighted indexing measurement to improve coal mining safety. METHODS: Qualitative and quantitative mixed research methodologies were adopted for this research, including analysis of two case studies, correlation analysis, and comparative analysis. The literature reviewed the most-used multi-criteria decision making (MCDM) methods, including subjective methods and objective methods. The advantages and disadvantages of each MCDM method were briefly discussed. One more round literature review was conducted to search publications between 2017 and 2019 in CNKI. Followed two case studies, correlation analysis and comparative analysis were then conducted. Research ethics was approved by the Shanxi Coking Coal Group Research Committee. RESULTS: The literature searched a total of 25,831publications and found that the PCA method was the predominant method adopted, and the Entropy method was the second most widely adopted method. Two weighting methods were compared using two case studies. For the comparative analysis of Case Study 1, the PCA method appeared to be more responsive than the Entropy. For Case Study 2, the Entropy method is more responsive than the PCA. As a result, both methods were adopted for different cases in the case study mine and finally deployed for user acceptance testing on 5 November 2020. CONCLUSIONS: The findings and suggestions were provided as further scopes for further research. This research indicated that no single method could be adopted as the better option for establishing indexing measurement in all cases. The practical implication suggests that comparative analysis should always be conducted on each case and determine the appropriate weighting method to the relevant case. This research recommended that the PCA method was a dimension reduction technique that could be handy for identifying the critical variables or factors and effectively used in hazard, risk, and emergency assessment. The PCA method might also be well-applied for developing predicting and forecasting systems as it was sensitive to outliers. The Entropy method might be suitable for all the cases requiring the MCDM. There is also a need to conduct further research to probe the causal reasons why the PCA and Entropy methods were applied to each case and not the other way round. This research found that the Entropy method provides higher accuracy than the PCA method. This research also found that the Entropy method demonstrated to assess the weights of the higher dimension dataset was higher sensitivity than the lower dimensions. Finally, the comprehensive analysis indicates a need to explore a more responsive method for establishing a weighted indexing measurement for warning applications in hazard, risk, and emergency assessments.

A novel intelligent system based on adjustable classifier models for diagnosing heart sounds.

A novel intelligent diagnostic system is proposed to diagnose heart sounds (HSs). The innovations of this system are primarily reflected in the automatic segmentation and extraction of the first complex sound [Formula: see text] and second complex sound [Formula: see text]; the automatic extraction of the secondary envelope-based diagnostic features [Formula: see text], [Formula: see text], and [Formula: see text] from [Formula: see text] and [Formula: see text]; and the adjustable classifier models that correspond to the confidence bounds of the Chi-square ([Formula: see text]) distribution and are adjusted by the given confidence levels (denoted as [Formula: see text]). The three stages of the proposed system are summarized as follows. In stage 1, the short time modified Hilbert transform (STMHT)-based curve is used to segment and extract [Formula: see text] and [Formula: see text]. In stage 2, the envelopes [Formula: see text] and [Formula: see text] for periods [Formula: see text] and [Formula: see text] are obtained via a novel method, and the frequency features are automatically extracted from [Formula: see text] and [Formula: see text] by setting different threshold value ([Formula: see text]) lines. Finally, the first three principal components determined based on principal component analysis (PCA) are used as the diagnostic features. In stage 3, a Gaussian mixture model (GMM)-based component objective function [Formula: see text] is generated. Then, the [Formula: see text] distribution for component k is determined by calculating the Mahalanobis distance from [Formula: see text] to the class mean [Formula: see text] for component k, and the confidence region of component k is determined by adjusting the optimal confidence level [Formula: see text] and used as the criterion to diagnose HSs. The performance evaluation was validated by sounds from online HS databases and clinical heart databases. The accuracy of the proposed method was compared to the accuracies of other state-of-the-art methods, and the highest classification accuracies of [Formula: see text], [Formula: see text], [Formula: see text], [Formula: see text], [Formula: see text], 99.67[Formula: see text] and 99.91[Formula: see text] in the detection of MR, MS, ASD, NM, AS, AR and VSD sounds were achieved by setting [Formula: see text] to 0.87,0.65,0.67,0.65,0.67,0.79 and 0.87, respectively.

A machine learning-based biological aging prediction and its associations with healthy lifestyles: the Dongfeng-Tongji cohort.

This study aims to establish a biological age (BA) predictor and to investigate the roles of lifestyles on biological aging. The 14,848 participants with the available information of multisystem measurements from the Dongfeng-Tongji cohort were used to estimate BA. We developed a composite BA predictor showing a high correlation with chronological age (CA) (r = 0.82) by using an extreme gradient boosting (XGBoost) algorithm. The average frequency hearing threshold, forced expiratory volume in 1 second (FEV1 ), gender, systolic blood pressure, and homocysteine ranked as the top five important features for the BA predictor. Two aging indexes, recorded as the AgingAccel (the residual from regressing predicted age on CA) and aging rate (the ratio of predicted age to CA), showed positive associations with the risks of all-cause (HR (95% CI) = 1.12 (1.10-1.14) and 1.08 (1.07-1.10), respectively) and cause-specific (HRs ranged from 1.06 to ∼1.15) mortality. Each 1-point increase in healthy lifestyle score (including normal body mass index, never smoking, moderate alcohol drinking, physically active, and sleep 7-9 h/night) was associated with a 0.21-year decrease in the AgingAccel (95% CI: -0.27 to -0.15) and a 0.4% decrease in the aging rate (95% CI: -0.5% to -0.3%). This study developed a machine learning-based BA predictor in a prospective Chinese cohort. Adherence to healthy lifestyles showed associations with delayed biological aging, which highlights potential preventive interventions.

Revisão taxonômica de Psalidodon eigenmanniorum (Cope) com base em análises morfométrica, merística e padrão de colorido (Characiformes: Characidae) / Taxonomic review of Psalidodon eigenmanniorum (Cope) based on morphometric and meristic analysis and color pattern (Characiformes: Characidae)

A identidade de Psalidodon eigenmanniorum (Cope, 1894) e a possibilidade de se constituir em mais de uma espécie é testada através de análises da morfometria (19 medidas), dos caracteres merísticos (14 contagens) e do padrão de colorido de 705 exemplares provenientes dos sistemas dos rios Tramandaí/Mampituba, da laguna dos Patos e drenagem do baixo rio Uruguai. Foram diafanizados e corados 40 exemplares. Os dados morfométricos foram utilizados na Análise de Componentes Principais, Análise Discriminante, Morfometria Geométrica e Função Discriminante. As análises foram feitas considerando os sexos em separado dentro de cada sistema hidrográfico, bem como comparando as populações entre os sistemas hidrográficos e finalmente no conjunto de sistemas representando a área de ocorrência da espécie. A partir dos dados analisados não foram encontradas diferenças entre os sexos. Os resultados mostraram variação morfológica que não sustenta o reconhecimento de possíveis espécies crípticas. A variação encontrada nos dados merísticos, morfométricos e no padrão de colorido justifica a redescrição da espécie. Os resultados das comparações entre as populações indicaram variações nesses caracteres indicando que a espécie possui considerável plasticidade fenotípica.(AU)

The identity of Psalidodon eigenmanniorum (Cope, 1894) and the possibility of constituting more than one species is tested through analyzes of morphometry (19 measurements), meristic characters (14 counts) and the color pattern of 705 specimens from the Tramandaí/Mampituba, from the Patos lagoon and from the lower Uruguay River drainage. Forty specimens were cleared and stained. Morphometric data were used in Principal Component Analysis, Discriminant Analysis, Geometric Morphometry and Discriminant Function. The analysis was carried out considering the sexes separately within each hydrographic system, as well as comparing the populations between the hydrographic systems and finally in the set of systems representing the area of occurrence of the species. No differences were found between the sexes in the analyzed data. The results showed morphological variation that does not support the recognition of possible cryptic species. The variation found in meristic, morphometric and color pattern data justifies the redescription of the species. The species is described to the aforementioned drainages, and the results demonstrate its phenotypic plasticity.(AU)

CNV analysis in a diagnostic setting using target panel.

OBJECTIVE: Copy-number variation (CNV) is an important source of genetic diversity in humans. It can cause Mendelian or sporadic traits or be associated with complex diseases by various molecular mechanisms, including gene dosage, gene disruption, gene fusion and position effects. In clinical diagnostics, it is therefore fundamental to be able to identify such variations. The preferred techniques for CNV detection are MLPA, aCGH and qPCR, which have proven to be valuable, and they are complex, costly and require prior knowledge of the region to analyze. CNV calling from NGS data still suffers from data variability. Coverage can vary greatly from one region of the genome to another, depending on many factors like complexity, GC content, repeated regions and many others. In this paper, we describe how we developed a method for CNV detection. MATERIALS AND METHODS: Our method exploits CoNVaDING to detect single- and multiple-exon CNVs in targeted NGS data. RESULTS: We demonstrated that our CNV analysis has 100% specificity and 99.998% sensitivity. We also show how we evaluated the performance of this method based on internal analysis. CONCLUSIONS: The results indicate that the method can be used to screen prior to standard labs technologies, thus reducing the number of analyses, as well as costs, and increasing test conclusiveness.

Sustainable development and health assessment model of higher education in India: A mathematical modeling approach.

The Coronavirus Disease 2019 has resulted in a transition from physical education to online learning, leading to a collapse of the established educational order and a wisdom test for the education governance system. As a country seriously affected by the pandemic, the health of the Indian higher education system urgently requires assessment to achieve sustainable development and maximize educational externalities. This research systematically proposes a health assessment model from four perspectives, including educational volume, efficiency, equality, and sustainability, by employing the Technique for Order Preference by Similarity to an Ideal Solution Model, Principal Component Analysis, DEA-Tobit Model, and Augmented Solow Model. Empirical results demonstrate that India has high efficiency and an absolute health score in the higher education system through multiple comparisons between India and the other selected countries while having certain deficiencies in equality and sustainability. Additionally, single-target and multiple-target path are simultaneously proposed to enhance the Indian current education system. The multiple-target approach of the India-China-Japan-Europe-USA process is more feasible to achieve sustainable development, which would improve the overall health score from .351 to .716. This finding also reveals that the changes are relatively complex and would take 91.5 years considering the relationship between economic growth rates and crucial indicators. Four targeted policies are suggested for each catching-up period, including expanding and increasing the social funding sources, striving for government expenditure support to improve infrastructures, imposing gender equality in education, and accelerating the construction of high-quality teachers.

AMMI and GGE biplot analysis for yield performance and stability assessment of selected Bambara groundnut (Vigna subterranea L. Verdc.) genotypes under the multi-environmental trails (METs).

The stability and high yielding of Vigna subterranea L. Verdc. genotype is an important factor for long-term development and food security. The effects of G × E interaction on yield stability in 30 Bambara groundnut genotypes in four different Malaysian environments were investigated in this research. The experiment used a randomized complete block design with three replications in each environment. Over multiple harvests, yield component traits such as the total number of pods per plant, fresh pods weight (g), hundred seeds weight (g), and yield per hectare were evaluated in the main and off-season in 2020 and 2021. Stability tests for multivariate stability parameters were performed based on analyses of variance. For all the traits, the pooled analysis of variance revealed highly significant (p < 0.01) variations between genotypes, locations, seasons, and genotypes by environment (G × E interaction). A two-dimensional GGE biplot was generated using the first two principal components (axis 1 and axis 2), which accounted for 94.97% and 3.11% difference in GEI for yield per hectare, respectively. Season and location were found to be the most significant causes of yield heterogeneity, accounting for 31.13% and 14.02% of overall G + E + G × E variation, respectively, according to the combined study of variance. The GGE biplot revealed that the three winning genotypes G1, G3, and G5 appear across environments whereas AMMI model exposed genotypes viz G18, G14, G7, G3, G1, and G5 as best performer. Based on ideal genotype ranking genotype G1 was the best performer, with a high mean yield and high stability in the tested environment. According to the AEC line, genotypes G1 and G3 were extremely stable, while genotypes G2 and G4 were low stable, with a high average yielding per hectare. A GGE and AMMI biplot graphically showed the interrelationships between the tested environment and genotypes, classified genotypes into three categories as well as simplifying visual evaluations, according to this investigation. According to our results, breeding could improve yield production, and the genotypes discovered could be recommended for commercial cultivation.

An Efficient PCA-GA-HKSVM-Based Disease Diagnostic Assistant.

Disease diagnosis faces challenges such as misdiagnosis, lack of diagnosis, and slow diagnosis. There are several machine learning techniques that have been applied to address these challenges, where a set of symptoms is applied to a classification model that predicts the presence or absence of a disease. To improve on the performance of these techniques, this paper presents a technique which involves feature selection using principal component analysis (PCA), a hybrid kernel-based support vector machine (HKSVM) classification model and hyperparameter optimization using genetic algorithm (GA). The HKSVM in this paper introduces a new way of combining three kernels: Radial basis function (RBF), linear, and polynomial. Combining local (RBF) and global (linear and polynomial) kernels has the effect of improved model performance. This is because the local kernels are better able to distinguish points closer to each other while the global kernels are more suited to distinguish points that are far away from each other. The PCA-GA-HKSVM is used on 7 different medical datasets, with two datasets being multiclass datasets and 5 datasets being binary. Performance evaluation metrics used were accuracy, precision, and recall. It was observed that the PCA-GA-HKSVM offered better performance than the single kernel support vector machines (SVMs).

The Application of Principal Component Analysis on Clinical and Biochemical Parameters Exemplified in Children With Congenital Adrenal Hyperplasia.

Purpose: Principal component analysis (PCA) is a mathematical model which simplifies data into new, combined variables. Optimal treatment of pediatric congenital adrenal hyperplasia (CAH) remains a challenge and requires evaluation of all biochemical and clinical markers. The aim of this study was to introduce PCA methodology as a tool to optimize management in a cohort of pediatric and adolescent patients with CAH by including adrenal steroid measurements and clinical parameters. Methods: This retrospective, longitudinal cohort of 33 children and adolescents with CAH due to 21-hydroxylase deficiency included 406 follow-up observations. PCAs were applied to serum hormone concentrations and compared to treatment efficacy evaluated by clinical parameters. Results: We provide and describe the first PCA models with hormone parameters denoted in sex- and age-adjusted standard deviation (SD) scores to comprehensibly describe the combined 'endocrine profiles' of patients with classical and non-classical CAH, respectively. Endocrine profile scores were predictive markers of treatment efficacy for classical (AUC=92%; accuracy 95%; p=1.8e-06) and non-classical CAH (AUC=80%; accuracy 91%; p=0.004). A combined PCA demonstrated clustering of patients with classical and non-classical CAH by serum 17-hydroxyprogesterone (17-OHP) and dehydroepiandrosterone-sulphate (DHEAS) concentrations. Conclusion: As an example of the possibilities of PCA, endocrine profiles were successfully able to distinguish between patients with CAH according to treatment efficacy and to elucidate biochemical differences between classical and non-classical CAH.

Clustering of Brain Tumor Based on Analysis of MRI Images Using Robust Principal Component Analysis (ROBPCA) Algorithm.

Automated detection of brain tumor location is essential for both medical and analytical uses. In this paper, we clustered brain MRI images to detect tumor location. To obtain perfect results, we presented an unsupervised robust PCA algorithm to clustered images. The proposed method clusters brain MR image pixels to four leverages. The algorithm is implemented for five brain diseases such as glioma, Huntington, meningioma, Pick, and Alzheimer's. We used ten images of each disease to validate the optimal identification rate. According to the results obtained, 2% of the data in the bad leverage part of the image were determined, which acceptably discerned the tumor. Results show that this method has the potential to detect tumor location for brain disease with high sensitivity. Moreover, results show that the method for the Glioma images has approximately better results than others. However, according to the ROC curve for all selected diseases, the present method can find lesion location.

QuantifyPolarity, a new tool-kit for measuring planar polarized protein distributions and cell properties in developing tissues.

The coordination of cells or structures within the plane of a tissue is known as planar polarization. It is often governed by the asymmetric distribution of planar polarity proteins within cells. A number of quantitative methods have been developed to provide a readout of planar polarized protein distributions. However, previous planar polarity quantification methods can be affected by variation in cell geometry. Hence, we developed a novel planar polarity quantification method based on Principal Component Analysis (PCA) that is shape insensitive. Here, we compare this method with other state-of-the-art methods on simulated models and biological datasets. We found that the PCA method performs robustly in quantifying planar polarity independently of variation in cell geometry and other image conditions. We designed a user-friendly graphical user interface called QuantifyPolarity, equipped with three polarity methods for automated quantification of polarity. QuantifyPolarity also provides tools to quantify cell morphology and packing geometry, allowing the relationship of these characteristics to planar polarization to be investigated. This tool enables experimentalists with no prior computational expertise to perform high-throughput cell polarity and shape analysis automatically and efficiently.

A spectral theory for Wright's inbreeding coefficients and related quantities.

Wright's inbreeding coefficient, FST, is a fundamental measure in population genetics. Assuming a predefined population subdivision, this statistic is classically used to evaluate population structure at a given genomic locus. With large numbers of loci, unsupervised approaches such as principal component analysis (PCA) have, however, become prominent in recent analyses of population structure. In this study, we describe the relationships between Wright's inbreeding coefficients and PCA for a model of K discrete populations. Our theory provides an equivalent definition of FST based on the decomposition of the genotype matrix into between and within-population matrices. The average value of Wright's FST over all loci included in the genotype matrix can be obtained from the PCA of the between-population matrix. Assuming that a separation condition is fulfilled and for reasonably large data sets, this value of FST approximates the proportion of genetic variation explained by the first (K - 1) principal components accurately. The new definition of FST is useful for computing inbreeding coefficients from surrogate genotypes, for example, obtained after correction of experimental artifacts or after removing adaptive genetic variation associated with environmental variables. The relationships between inbreeding coefficients and the spectrum of the genotype matrix not only allow interpretations of PCA results in terms of population genetic concepts but extend those concepts to population genetic analyses accounting for temporal, geographical and environmental contexts.

Cuticular Hydrocarbon Plasticity in Three Rice Planthopper Species.

Insect cuticular hydrocarbons (CHCs) are organic compounds of the surface lipid layer, which function as a barrier against water loss and xenobiotic penetration, while also serving as chemical signals. Plasticity of CHC profiles can vary depending upon numerous biological and environmental factors. Here, we investigated potential sources of variation in CHC profiles of Nilaparvata lugens, Laodelphax striatellus and Sogatella furcifera, which are considered to be the most important rice pests in Asia. CHC profiles were quantified by GC/MS, and factors associated with variations were explored by conducting principal component analysis (PCA). Transcriptomes were further compared under different environmental conditions. The results demonstrated that CHC profiles differ among three species and change with different developmental stages, sexes, temperature, humidity and host plants. Genes involved in cuticular lipid biosynthesis pathways are modulated, which might explain why CHC profiles vary among species under different environments. Our study illustrates some biological and ecological variations in modifying CHC profiles, and the underlying molecular regulation mechanisms of the planthoppers in coping with changes of environmental conditions, which is of great importance for identifying potential vulnerabilities relating to pest ecology and developing novel pest management strategies.

Classification of ECG signals using multi-cumulants based evolutionary hybrid classifier.

Every human being has a different electro-cardio-graphy (ECG) waveform that provides information about the well being of a human heart. Therefore, ECG waveform can be used as an effective identification measure in biometrics and many such applications of human identification. To achieve fast and accurate identification of human beings using ECG signals, a novel robust approach has been introduced here. The databases of ECG utilized during the experimentation are MLII, UCI repository arrhythmia and PTBDB databases. All these databases are imbalanced; hence, resampling techniques are helpful in making the databases balanced. Noise removal is performed with discrete wavelet transform (DWT) and features are obtained with multi-cumulants. This approach is mainly based on features extracted from the ECG data in terms of multi-cumulants. The multi-cumulants feature based ECG data is classified using kernel extreme learning machine (KELM). The parameters of multi-cumulants and KELM are optimized using genetic algorithm (GA). Excellent classification rate is achieved with 100% accuracy on MLII and UCI repository arrhythmia databases, and 99.57% on PTBDB database. Comparison with existing state-of-art approaches has also been performed to prove the efficacy of the proposed approach. Here, the process of classification in the proposed approach is named as evolutionary hybrid classifier.

Principal component analysis is limited to low-resolution analysis in cryoEM.

Principal component analysis (PCA) has been widely proposed to analyze flexibility and heterogeneity in cryo-electron microscopy (cryoEM). In this paper, it is argued that (i) PCA is an excellent technique to describe continuous flexibility at low resolution (but not so much at high resolution) and (ii) PCA components should be analyzed in a concerted manner (and not independently).

Exploring the chemical space of protein-protein interaction inhibitors through machine learning.

Although protein-protein interactions (PPIs) have emerged as the basis of potential new therapeutic approaches, targeting intracellular PPIs with small molecule inhibitors is conventionally considered highly challenging. Driven by increasing research efforts, success rates have increased significantly in recent years. In this study, we analyze the physicochemical properties of 9351 non-redundant inhibitors present in the iPPI-DB and TIMBAL databases to define a computational model for active compounds acting against PPI targets. Principle component analysis (PCA) and k-means clustering were used to identify plausible PPI targets in regions of interest in the active group in the chemical space between active and inactive iPPI compounds. Notably, the uniquely defined active group exhibited distinct differences in activity compared with other active compounds. These results demonstrate that active compounds with regions of interest in the chemical space may be expected to provide insights into potential PPI inhibitors for particular protein targets.

Exploring the distribution of statistical feature parameters for natural sound textures.

Sounds like "running water" and "buzzing bees" are classes of sounds which are a collective result of many similar acoustic events and are known as "sound textures". A recent psychoacoustic study using sound textures has reported that natural sounding textures can be synthesized from white noise by imposing statistical features such as marginals and correlations computed from the outputs of cochlear models responding to the textures. The outputs being the envelopes of bandpass filter responses, the 'cochlear envelope'. This suggests that the perceptual qualities of many natural sounds derive directly from such statistical features, and raises the question of how these statistical features are distributed in the acoustic environment. To address this question, we collected a corpus of 200 sound textures from public online sources and analyzed the distributions of the textures' marginal statistics (mean, variance, skew, and kurtosis), cross-frequency correlations and modulation power statistics. A principal component analysis of these parameters revealed a great deal of redundancy in the texture parameters. For example, just two marginal principal components, which can be thought of as measuring the sparseness or burstiness of a texture, capture as much as 64% of the variance of the 128 dimensional marginal parameter space, while the first two principal components of cochlear correlations capture as much as 88% of the variance in the 496 correlation parameters. Knowledge of the statistical distributions documented here may help guide the choice of acoustic stimuli with high ecological validity in future research.