Cerebellar Neurodynamics Predict Decision Timing and Outcome on the Single-Trial Level.

Goal-directed behavior requires the interaction of multiple brain regions. How these regions and their interactions with brain-wide activity drive action selection is less understood. We have investigated this question by combining whole-brain volumetric calcium imaging using light-field microscopy and an operant-conditioning task in larval zebrafish. We find global, recurring dynamics of brain states to exhibit pre-motor bifurcations toward mutually exclusive decision outcomes. These dynamics arise from a distributed network displaying trial-by-trial functional connectivity changes, especially between cerebellum and habenula, which correlate with decision outcome. Within this network the cerebellum shows particularly strong and predictive pre-motor activity (>10 s before movement initiation), mainly within the granule cells. Turn directions are determined by the difference neuroactivity between the ipsilateral and contralateral hemispheres, while the rate of bi-hemispheric population ramping quantitatively predicts decision time on the trial-by-trial level. Our results highlight a cognitive role of the cerebellum and its importance in motor planning.

The training process of many deep networks explores the same low-dimensional manifold.

We develop information-geometric techniques to analyze the trajectories of the predictions of deep networks during training. By examining the underlying high-dimensional probabilistic models, we reveal that the training process explores an effectively low-dimensional manifold. Networks with a wide range of architectures, sizes, trained using different optimization methods, regularization techniques, data augmentation techniques, and weight initializations lie on the same manifold in the prediction space. We study the details of this manifold to find that networks with different architectures follow distinguishable trajectories, but other factors have a minimal influence; larger networks train along a similar manifold as that of smaller networks, just faster; and networks initialized at very different parts of the prediction space converge to the solution along a similar manifold.

Specialized proresolving mediator resolvin E1 corrects the altered cystic fibrosis nasal epithelium cilia beating dynamics.

In cystic fibrosis (CF), impaired mucociliary clearance leads to chronic infection and inflammation. However, cilia beating features in a CF altered environment, consisting of dehydrated airway surface liquid layer and abnormal mucus, have not been fully characterized. Furthermore, acute inflammation is normally followed by an active resolution phase requiring specialized proresolving lipid mediators (SPMs) and allowing return to homeostasis. However, altered SPMs biosynthesis has been reported in CF. Here, we explored cilia beating dynamics in CF airways primary cultures and its response to the SPMs, resolvin E1 (RvE1) and lipoxin B4 (LXB4). Human nasal epithelial cells (hNECs) from CF and non-CF donors were grown at air-liquid interface. The ciliary beat frequency, synchronization, orientation, and density were analyzed from high-speed video microscopy using a multiscale Differential Dynamic Microscopy algorithm and an in-house developed method. Mucins and ASL layer height were studied by qRT-PCR and confocal microscopy. Principal component analysis showed that CF and non-CF hNEC had distinct cilia beating phenotypes, which was mostly explained by differences in cilia beat organization rather than frequency. Exposure to RvE1 (10 nM) and to LXB4 (10 nM) restored a non-CF-like cilia beating phenotype. Furthermore, RvE1 increased the airway surface liquid (ASL) layer height and reduced the mucin MUC5AC thickness. The calcium-activated chloride channel, TMEM16A, was involved in the RvE1 effect on cilia beating, hydration, and mucus. Altogether, our results provide evidence for defective cilia beating in CF airway epithelium and a role of RvE1 and LXB4 to restore the main epithelial functions involved in the mucociliary clearance.

Multilevel superposition for deciphering the conformational variability of protein ensembles.

The dynamics and variability of protein conformations are directly linked to their functions. Many comparative studies of X-ray protein structures have been conducted to elucidate the relevant conformational changes, dynamics and heterogeneity. The rapid increase in the number of experimentally determined structures has made comparison an effective tool for investigating protein structures. For example, it is now possible to compare structural ensembles formed by enzyme species, variants or the type of ligands bound to them. In this study, the author developed a multilevel model for estimating two covariance matrices that represent inter- and intra-ensemble variability in the Cartesian coordinate space. Principal component analysis using the two estimated covariance matrices identified the inter-/intra-enzyme variabilities, which seemed to be important for the enzyme functions, with the illustrative examples of cytochrome P450 family 2 enzymes and class A $\beta$-lactamases. In P450, in which each enzyme has its own active site of a distinct size, an active-site motion shared universally between the enzymes was captured as the first principal mode of the intra-enzyme covariance matrix. In this case, the method was useful for understanding the conformational variability after adjusting for the differences between enzyme sizes. The developed method is advantageous in small ensemble-size problems and hence promising for use in comparative studies on experimentally determined structures where ensemble sizes are smaller than those generated, for example, by molecular dynamics simulations.

Projection-based two-sample inference for sparsely observed multivariate functional data.

Modern longitudinal studies collect multiple outcomes as the primary endpoints to understand the complex dynamics of the diseases. Oftentimes, especially in clinical trials, the joint variation among the multidimensional responses plays a significant role in assessing the differential characteristics between two or more groups, rather than drawing inferences based on a single outcome. We develop a projection-based two-sample significance test to identify the population-level difference between the multivariate profiles observed under a sparse longitudinal design. The methodology is built upon widely adopted multivariate functional principal component analysis to reduce the dimension of the infinite-dimensional multi-modal functions while preserving the dynamic correlation between the components. The test applies to a wide class of (non-stationary) covariance structures of the response, and it detects a significant group difference based on a single p-value, thereby overcoming the issue of adjusting for multiple p-values that arise due to comparing the means in each of components separately. Finite-sample numerical studies demonstrate that the test maintains the type-I error, and is powerful to detect significant group differences, compared to the state-of-the-art testing procedures. The test is carried out on two significant longitudinal studies for Alzheimer's disease and Parkinson's disease (PD) patients, namely, TOMMORROW study of individuals at high risk of mild cognitive impairment to detect differences in the cognitive test scores between the pioglitazone and the placebo groups, and Azillect study to assess the efficacy of rasagiline as a potential treatment to slow down the progression of PD.

Functional support vector machine.

Linear and generalized linear scalar-on-function modeling have been commonly used to understand the relationship between a scalar response variable (e.g. continuous, binary outcomes) and functional predictors. Such techniques are sensitive to model misspecification when the relationship between the response variable and the functional predictors is complex. On the other hand, support vector machines (SVMs) are among the most robust prediction models but do not take account of the high correlations between repeated measurements and cannot be used for irregular data. In this work, we propose a novel method to integrate functional principal component analysis with SVM techniques for classification and regression to account for the continuous nature of functional data and the nonlinear relationship between the scalar response variable and the functional predictors. We demonstrate the performance of our method through extensive simulation experiments and two real data applications: the classification of alcoholics using electroencephalography signals and the prediction of glucobrassicin concentration using near-infrared reflectance spectroscopy. Our methods especially have more advantages when the measurement errors in functional predictors are relatively large.

Identifying potential small molecule-miRNA associations via Robust PCA based on γ-norm regularization.

Dysregulation of microRNAs (miRNAs) is closely associated with refractory human diseases, and the identification of potential associations between small molecule (SM) drugs and miRNAs can provide valuable insights for clinical treatment. Existing computational techniques for inferring potential associations suffer from limitations in terms of accuracy and efficiency. To address these challenges, we devise a novel predictive model called RPCA$\Gamma $NR, in which we propose a new Robust principal component analysis (PCA) framework based on $\gamma $-norm and $l_{2,1}$-norm regularization and design an Augmented Lagrange Multiplier method to optimize it, thereby deriving the association scores. The Gaussian Interaction Profile Kernel Similarity is calculated to capture the similarity information of SMs and miRNAs in known associations. Through extensive evaluation, including Cross Validation Experiments, Independent Validation Experiment, Efficiency Analysis, Ablation Experiment, Matrix Sparsity Analysis, and Case Studies, RPCA$\Gamma $NR outperforms state-of-the-art models concerning accuracy, efficiency and robustness. In conclusion, RPCA$\Gamma $NR can significantly streamline the process of determining SM-miRNA associations, thus contributing to advancements in drug development and disease treatment.

A comparison between similarity matrices for principal component analysis to assess population stratification in sequenced genetic data sets.

Genetic similarity matrices are commonly used to assess population substructure (PS) in genetic studies. Through simulation studies and by the application to whole-genome sequencing (WGS) data, we evaluate the performance of three genetic similarity matrices: the unweighted and weighted Jaccard similarity matrices and the genetic relationship matrix. We describe different scenarios that can create numerical pitfalls and lead to incorrect conclusions in some instances. We consider scenarios in which PS is assessed based on loci that are located across the genome ('globally') and based on loci from a specific genomic region ('locally'). We also compare scenarios in which PS is evaluated based on loci from different minor allele frequency bins: common (>5%), low-frequency (5-0.5%) and rare (<0.5%) single-nucleotide variations (SNVs). Overall, we observe that all approaches provide the best clustering performance when computed based on rare SNVs. The performance of the similarity matrices is very similar for common and low-frequency variants, but for rare variants, the unweighted Jaccard matrix provides preferable clustering features. Based on visual inspection and in terms of standard clustering metrics, its clusters are the densest and the best separated in the principal component analysis of variants with rare SNVs compared with the other methods and different allele frequency cutoffs. In an application, we assessed the role of rare variants on local and global PS, using WGS data from multiethnic Alzheimer's disease data sets and European or East Asian populations from the 1000 Genome Project.

Integrating multiple traits for improving polygenic risk prediction in disease and pharmacogenomics GWAS.

Polygenic risk score (PRS) has been recently developed for predicting complex traits and drug responses. It remains unknown whether multi-trait PRS (mtPRS) methods, by integrating information from multiple genetically correlated traits, can improve prediction accuracy and power for PRS analysis compared with single-trait PRS (stPRS) methods. In this paper, we first review commonly used mtPRS methods and find that they do not directly model the underlying genetic correlations among traits, which has been shown to be useful in guiding multi-trait association analysis in the literature. To overcome this limitation, we propose a mtPRS-PCA method to combine PRSs from multiple traits with weights obtained from performing principal component analysis (PCA) on the genetic correlation matrix. To accommodate various genetic architectures covering different effect directions, signal sparseness and across-trait correlation structures, we further propose an omnibus mtPRS method (mtPRS-O) by combining P values from mtPRS-PCA, mtPRS-ML (mtPRS based on machine learning) and stPRSs using Cauchy Combination Test. Our extensive simulation studies show that mtPRS-PCA outperforms other mtPRS methods in both disease and pharmacogenomics (PGx) genome-wide association studies (GWAS) contexts when traits are similarly correlated, with dense signal effects and in similar effect directions, and mtPRS-O is consistently superior to most other methods due to its robustness under various genetic architectures. We further apply mtPRS-PCA, mtPRS-O and other methods to PGx GWAS data from a randomized clinical trial in the cardiovascular domain and demonstrate performance improvement of mtPRS-PCA in both prediction accuracy and patient stratification as well as the robustness of mtPRS-O in PRS association test.

The interictal suppression hypothesis is the dominant differentiator of seizure onset zones in focal epilepsy.

Successful surgical treatment of drug-resistant epilepsy traditionally relies on the identification of seizure onset zones (SOZs). Connectome-based analyses of electrographic data from stereo electroencephalography (SEEG) may empower improved detection of SOZs. Specifically, connectome-based analyses based on the interictal suppression hypothesis posit that when the patient is not having a seizure, SOZs are inhibited by non-SOZs through high inward connectivity and low outward connectivity. However, it is not clear whether there are other motifs that can better identify potential SOZs. Thus, we sought to use unsupervised machine learning to identify network motifs that elucidate SOZs and investigate if there is another motif that outperforms the ISH. Resting-state SEEG data from 81 patients with drug-resistant epilepsy undergoing a pre-surgical evaluation at Vanderbilt University Medical Center were collected. Directed connectivity matrices were computed using the alpha band (8-13 Hz). Principal component analysis (PCA) was performed on each patient's connectivity matrix. Each patient's components were analysed qualitatively to identify common patterns across patients. A quantitative definition was then used to identify the component that most closely matched the observed pattern in each patient. A motif characteristic of the interictal suppression hypothesis (high-inward and low-outward connectivity) was present in all individuals and found to be the most robust motif for identification of SOZs in 64/81 (79%) patients. This principal component demonstrated significant differences in SOZs compared to non-SOZs. While other motifs for identifying SOZs were present in other patients, they differed for each patient, suggesting that seizure networks are patient specific, but the ISH is present in nearly all networks. We discovered that a potentially suppressive motif based on the interictal suppression hypothesis was present in all patients, and it was the most robust motif for SOZs in 79% of patients. Each patient had additional motifs that further characterized SOZs, but these motifs were not common across all patients. This work has the potential to augment clinical identification of SOZs to improve epilepsy treatment.

VCF2PCACluster: a simple, fast and memory-efficient tool for principal component analysis of tens of millions of SNPs.

Principal component analysis (PCA) is an important and widely used unsupervised learning method that determines population structure based on genetic variation. Genome sequencing of thousands of individuals usually generate tens of millions of SNPs, making it challenging for PCA analysis and interpretation. Here we present VCF2PCACluster, a simple, fast and memory-efficient tool for Kinship estimation, PCA and clustering analysis, and visualization based on VCF formatted SNPs. We implemented five Kinship estimation methods and three clustering methods for its users to choose from. Moreover, unlike other PCA tools, VCF2PCACluster possesses a clustering function based on PCA result, which enabling users to automatically and clearly know about population structure. We demonstrated the same accuracy but a higher performance of this tool in performing PCA analysis on tens of millions of SNPs compared to another popular PLINK2 software, especially in peak memory usage that is independent of the number of SNPs in VCF2PCACluster.

Predicting stroke occurrences: a stacked machine learning approach with feature selection and data preprocessing.

Stroke prediction remains a critical area of research in healthcare, aiming to enhance early intervention and patient care strategies. This study investigates the efficacy of machine learning techniques, particularly principal component analysis (PCA) and a stacking ensemble method, for predicting stroke occurrences based on demographic, clinical, and lifestyle factors. We systematically varied PCA components and implemented a stacking model comprising random forest, decision tree, and K-nearest neighbors (KNN).Our findings demonstrate that setting PCA components to 16 optimally enhanced predictive accuracy, achieving a remarkable 98.6% accuracy in stroke prediction. Evaluation metrics underscored the robustness of our approach in handling class imbalance and improving model performance, also comparative analyses against traditional machine learning algorithms such as SVM, logistic regression, and Naive Bayes highlighted the superiority of our proposed method.

Analyzing postprandial metabolomics data using multiway models: a simulation study.

BACKGROUND: Analysis of time-resolved postprandial metabolomics data can improve the understanding of metabolic mechanisms, potentially revealing biomarkers for early diagnosis of metabolic diseases and advancing precision nutrition and medicine. Postprandial metabolomics measurements at several time points from multiple subjects can be arranged as a subjects by metabolites by time points array. Traditional analysis methods are limited in terms of revealing subject groups, related metabolites, and temporal patterns simultaneously from such three-way data. RESULTS: We introduce an unsupervised multiway analysis approach based on the CANDECOMP/PARAFAC (CP) model for improved analysis of postprandial metabolomics data guided by a simulation study. Because of the lack of ground truth in real data, we generate simulated data using a comprehensive human metabolic model. This allows us to assess the performance of CP models in terms of revealing subject groups and underlying metabolic processes. We study three analysis approaches: analysis of fasting-state data using principal component analysis, T0-corrected data (i.e., data corrected by subtracting fasting-state data) using a CP model and full-dynamic (i.e., full postprandial) data using CP. Through extensive simulations, we demonstrate that CP models capture meaningful and stable patterns from simulated meal challenge data, revealing underlying mechanisms and differences between diseased versus healthy groups. CONCLUSIONS: Our experiments show that it is crucial to analyze both fasting-state and T0-corrected data for understanding metabolic differences among subject groups. Depending on the nature of the subject group structure, the best group separation may be achieved by CP models of T0-corrected or full-dynamic data. This study introduces an improved analysis approach for postprandial metabolomics data while also shedding light on the debate about correcting baseline values in longitudinal data analysis.

A novel method for cell deconvolution using DNA methylation in PCA space.

BACKGROUND: In this study, we present a novel method for reference-based cell deconvolution using data from DNA methylation arrays. Different from existing methods like IDOL-Ext, which operate on probe-level data, our approach represents features in the principal component analysis (PCA) space for cell type deconvolution. RESULTS: Our method's accuracy in estimating cell compositions is validated across various public datasets, including blood samples from glioma patients. It demonstrates precision comparable to IDOL-Ext, with R2 values ranging from 0.73 to 0.99 for most cell types, while offering improved discrimination between similar cell types, particularly T cell subtypes in glioma patient samples (R2 0.42-0.75 vs. 0.36-0.66 for IDOL-Ext). However, both methods showed lower accuracy for certain cell types, such as memory CD8 T cells in glioma patients (R2 0.42 vs. 0.36 for IDOL-Ext), highlighting the challenges in distinguishing closely related cell populations. We have made this method available as an R package "BloodCellDecon" on GitHub. CONCLUSIONS: Our study confirms the efficacy of cell type deconvolution in PCA space. The results indicate wide-ranging applicability and potential for adaptation to other forms of genomic data.

Population-genetic and comparative interpopulation studies of the 15 autosomal STR markers in the population living in the Northwest of Iran.

INTRODUCTION: Iran, a country in the Middle East, has several ethnic and ethno-religious groups and needs its own ethnic-specific databases for the forensic statistical parameters and allele frequency of STR markers. METHODS: We have investigated 600 unrelated Turk individuals from four northwestern provinces of Iran using the Identifiler™ system (TPOX, FGA, vWA, TH01, CSF1PO, D2S1338, D3S1358, D5S818, D7S820, D8S1179, D13S317, D16S539, D18S51, D19S433, and D21S11). Furthermore, STR allelic frequencies were compared to previously population-based data. RESULTS AND CONCLUSION: After Bonferroni correction, deviation from Hardy-Weinberg equilibrium (HWE) was observed in the FGA, TPOX, VWA, and D19S433 loci (P value < 0.05). The combined power of discrimination (CPD) and exclusion (CPE) values for all 15 STR loci were 0.9999999999999999999984 and 0.9999999, respectively. In comparison with Azerbaijani and Turkish populations, there were no significant differences on all STR markers. However, in the Chinese Han population, differences at 13 STR loci were detected. Additionally, comparisons of Fischer genetic distance indices (FST) P-values did not reveal any statistically significant difference between Northwestern Iran, Azerbaijan and Iran (Fars) populations. PCA and PCoA analyses showed that our population was grouped with different populations in different quarters, showing a positive and negative correlation, respectively. In the NJ and UPGMA phylogenetic trees, Iranian populations were grouped together. These results demonstrated that the given set of STR markers can be confidently used for all identification tests in Northwestern Iran.

Principal component analysis as an efficient method for capturing multivariate brain signatures of complex disorders-ENIGMA study in people with bipolar disorders and obesity.

Multivariate techniques better fit the anatomy of complex neuropsychiatric disorders which are characterized not by alterations in a single region, but rather by variations across distributed brain networks. Here, we used principal component analysis (PCA) to identify patterns of covariance across brain regions and relate them to clinical and demographic variables in a large generalizable dataset of individuals with bipolar disorders and controls. We then compared performance of PCA and clustering on identical sample to identify which methodology was better in capturing links between brain and clinical measures. Using data from the ENIGMA-BD working group, we investigated T1-weighted structural MRI data from 2436 participants with BD and healthy controls, and applied PCA to cortical thickness and surface area measures. We then studied the association of principal components with clinical and demographic variables using mixed regression models. We compared the PCA model with our prior clustering analyses of the same data and also tested it in a replication sample of 327 participants with BD or schizophrenia and healthy controls. The first principal component, which indexed a greater cortical thickness across all 68 cortical regions, was negatively associated with BD, BMI, antipsychotic medications, and age and was positively associated with Li treatment. PCA demonstrated superior goodness of fit to clustering when predicting diagnosis and BMI. Moreover, applying the PCA model to the replication sample yielded significant differences in cortical thickness between healthy controls and individuals with BD or schizophrenia. Cortical thickness in the same widespread regional network as determined by PCA was negatively associated with different clinical and demographic variables, including diagnosis, age, BMI, and treatment with antipsychotic medications or lithium. PCA outperformed clustering and provided an easy-to-use and interpret method to study multivariate associations between brain structure and system-level variables. PRACTITIONER POINTS: In this study of 2770 Individuals, we confirmed that cortical thickness in widespread regional networks as determined by principal component analysis (PCA) was negatively associated with relevant clinical and demographic variables, including diagnosis, age, BMI, and treatment with antipsychotic medications or lithium. Significant associations of many different system-level variables with the same brain network suggest a lack of one-to-one mapping of individual clinical and demographic factors to specific patterns of brain changes. PCA outperformed clustering analysis in the same data set when predicting group or BMI, providing a superior method for studying multivariate associations between brain structure and system-level variables.

Compressed representation of brain genetic transcription.

The architecture of the brain is too complex to be intuitively surveyable without the use of compressed representations that project its variation into a compact, navigable space. The task is especially challenging with high-dimensional data, such as gene expression, where the joint complexity of anatomical and transcriptional patterns demands maximum compression. The established practice is to use standard principal component analysis (PCA), whose computational felicity is offset by limited expressivity, especially at great compression ratios. Employing whole-brain, voxel-wise Allen Brain Atlas transcription data, here we systematically compare compressed representations based on the most widely supported linear and non-linear methods-PCA, kernel PCA, non-negative matrix factorisation (NMF), t-stochastic neighbour embedding (t-SNE), uniform manifold approximation and projection (UMAP), and deep auto-encoding-quantifying reconstruction fidelity, anatomical coherence, and predictive utility across signalling, microstructural, and metabolic targets, drawn from large-scale open-source MRI and PET data. We show that deep auto-encoders yield superior representations across all metrics of performance and target domains, supporting their use as the reference standard for representing transcription patterns in the human brain.

Synthesis of isoniazid derivatives and evaluation of their α-chymotrypsin inhibitory effect through in silico guided in vitro studies.

Alpha-chymotrypsin is a serine protease. Its overexpression is responsible for several ailments, such as chronic obstructive pulmonary disease, autoimmune diseases, pancreatitis, and colon cancers. Therefore, the discovery of potent α-chymotrypsin inhibitors is essential for the treatment of the aforementioned ailments. In this study, we identified new α-chymotrypsin inhibitors through a systematic approach, utilizing the in silico and in vivo studies to predict and confirm the inhibitory potential of isoniazid derivatives. During this study, six compounds 2, 3, 4, 7, 9, and 10 were shortlisted from ten isoniazid derivatives through in silico screening. After that, MD simulations were performed for these compounds. The shortlisted compounds were evaluated through an in vitro α-chymotrypsin inhibitory assay. Compounds 9 and 10 showed a potent inhibition against α-chymotrypsin. The identified compounds or their derivatives can be further investigated as drug leads against the ailments caused by α-chymotrypsin and related serine proteases.

Computational exploration of compounds in Xylocarpus granatum as a potential inhibitor of Plasmodium berghei using docking, molecular dynamics, and DFT studies.

Malaria remains a global health concern, with the emergence of resistance to the antimalarial drug atovaquone through cytochrome b (cyt b) being well-documented. This study was prompted by the presence of this mutation in cyt b to enable new drug candidates capable of overcoming drug resistance. Our objective was to identify potential drug candidates from compounds of Xylocarpus granatum by computationally assessing their interactions with Plasmodium berghei cyt b. Using computational methods, we modeled cyt b (GenBank: AF146076.1), identified the binding cavity, and analyzed the Ramachandran plot against cyt b. Additionally, we conducted drug-likeness and absorption, distribution, metabolism, excretion, and toxicity (ADMET) studies, along with density functional theory (DFT) analysis of the compounds. Molecular docking and molecular dynamics simulation (MDS) were used to evaluate the binding energy and stability of the cyt b-ligand complex. Notably, our investigation highlighted kaempferol as a promising compound due to its high binding energy of 7.67 kcal/mol among all X. granatum compounds, coupled with favorable pharmacological properties (ADMET) and antiprotozoal properties at Pa 0.345 > Pi 0.009 (PASS value). DFT analysis showed that kaempferol has an energy gap of 4.514 eV. MDS indicated that all tested ligands caused changes in bonding and affected the structural conformation of cyt b, as observed before MDS (0 ns) and after MDS (100 ns). The most notable differences were observed in the types of hydrogen bonds between 0 and 100 ns. Nevertheles, MDS results from a 100 ns simulation revealed consistent behavior for kaempferol across various parameters including root mean square deviation (RMSD), root mean square fluctuation (RMSF), radius of gyration (Rg), solvent-accessible surface area (SASA), molecular mechanics-Poisson Boltzmann surface area (MM-PBSA), and hydrogen bonds. The cyt b-kaempferol complex demonstrated favorable energy stability, as supported by the internal energy distribution values observed in principal component analysis (PCA), which closely resembled those of the atovaquone control. Additionally, trajectory stability analysis indicated structural stability, with a cumulative eigenvalue of 24.7 %. Dynamic cross-correlation matrix (DCCM) analysis revealed a positive correlation among catalytic cytochrome residues within the amino acid residues range 119-268. The results of our research indicate that the structure of kaempferol holds promise as a potential candidate against Plasmodium.

Kinetic and thermodynamic analysis of alizarin Red S biosorption by Alhagi maurorum: a sustainable approach for water treatment.

Synthetic dyes, such as Alizarin Red S, contribute significantly to environmental pollution. This study investigates the biosorption potential of Alhagi maurorum biosorbent for the removal of Alizarin Red S (ARS) from aqueous solutions. Fourier transform infrared spectroscopy (FTIR) was used to analyze the biosorbent's adsorption sites. Various parameters were optimized to maximize dye adsorption. An optimal removal efficiency of 82.26% was attained by employing 0.9 g of biosorbent with a 25 ppm dye concentration at pH 6 and 60 °C over 30 min. The data were modeled using various isothermal and kinetic models to understand the adsorption behavior. Thermodynamic parameters indicated that the adsorption process was spontaneous and endothermic. The pseudo-second-order kinetic model best described the data, indicating chemisorption as the rate-limiting step. The data matched best to the Langmuir model, indicating that the adsorption occurs as a monolayer on uniform surfaces with a finite number of binding sites. The model showed a strong correlation (R² = 0.991) and a maximum adsorption capacity (qmax) of 8.203 mg/g. Principal component analysis (PCA) identified temperature as the dominant factor, with the primary component, PC1 capturing 100% of its effect. The mechanisms involved in ARS biosorption on A. maurorum include electrostatic interactions, hydrogen bonding, hydrophobic interactions, dipole-dipole interactions, and π-π stacking. Alhagi maurorum showed promising potential for biosorbing toxic dyes from contaminated water, suggesting further investigation for practical applications.