Covariance Structure Research Articles

502 Cat and mouse: The effect of consuming four different diet formants (intact mice, raw, canned wet and kibble) on energy expended to consume a meal and immediately post-prandial in domestic cats

Abstract Cats are obligate carnivores; however, we feed them a variety of different diet formats without a clear understanding of feeding behavior and metabolism among diet formats. Whole-prey or raw diets may mimic a more natural diet and require more energy to consume. However, there is currently a dearth of literature examining feline energy expenditure (EE) during the consumption of and immediate post-prandial period when fed different diet formats. The present study aimed to investigate differences in the energetics of adult cats consuming four different diet formats in isocaloric offerings: raw intact mice, commercial raw, canned wet, and kibble diets. Adult neutered male cats [n = 12; body weight (BW) = 4.82 ± 0.164 kg) were used in a 4 x 4 replicated Latin square design and adapted to consuming each diet format before the initiation of the study. Following a 2-d adaptation period, cats were fed 25% of their daily caloric requirement (50.77 ± 1.549 kcal) in indirect calorimetry chambers in which VO2 and VCO2 exchange were measured. Energy expenditure (kcal/BW0.67 kg) was calculated for the consumption period, 15 min immediately post-prandial and 2 h post-prandial. Respiratory gases were measured for one fasted reading, followed by the first feeding. During each feeding, measurements were taken during the consumption period and 15 min post-prandial. For the following 2 h, respiratory gases were measured every 25 min for an additional 4 measures of respiratory gases. All data were analyzed using a generalized linear mixed model considering the effects of treatment, time and their interaction as fixed, and the effects of cat and period as random. A spatial power covariance structure was used to model repeated measures. Significance was considered at P < 0.05 and Tukey’s post-hoc test was performed to separate means. Energy expenditure during consumption and 15 min post-prandial was similar among treatments, and EE for 15 min post-prandial was greater than EE during consumption (P < 0.001). During the 2 h post-prandial, EE for the mice diet was greater than the kibble and canned wet diets (P < 0.001), while EE for the raw diet was only greater than the kibble diet (P < 0.001). Energy expenditure was similar between the kibble and canned wet diets (P = 0.919) and between the mice and raw diets (P = 0.598). Among all treatments and time periods, the mouse diet had the greatest mean EE. An increase in EE during the feeding period and post-prandial could benefit sedentary indoor cats who may be in positive energy balance. Feeding cats a naturalistic diet, such as intact mice or a raw diet, may increase activity levels and EE, but the longer-term effects of these feeding formats on energy balance need to be explored.

308 Isoenergetic reduction of dietary macronutrients reveals distinct fecal metabolomic signatures in lean and obese cats fed at maintenance

Abstract Obesity has become a concerning problem in cats. Increasing evidence demonstrates the utility of metabolomics in identifying disease biomarkers and assessing the effects of nutritional interventions, although limited information exists regarding alterations in fecal metabolites in lean and obese domestic cats in response to different macronutrient content. In the current study, three extruded diets were formulated based on adult maintenance using an isoenergentic approach. These diets were created by adjusting the levels of identical ingredients to achieve a low-protein [LP; protein 28% metabolizable energy (ME), fat 40%ME, NFE 32% ME), a low-fat (LF; protein 42% ME, fat 30% ME, NFE 30% ME), and a low-carbohydrate (LC; protein 36% ME, fat 41% ME, NFE 23% ME] diet. Healthy neutered male adult cats [n = 18; body condition scores (BCS) 4 or 5 (lean, n = 9) and 8 or 9 (obese, n = 9)] were fed each diet for a period of 4 wk in a 3 x 3 Latin square design. Fecal samples from each cat were collected between d 22 and 28 of each treatment period. A total of 67 fecal metabolites in six groups (35 amino acids, 11 fatty acids, 10 sugars and sugar metabolites, 5 alcohols, 3 nitrogenous bases, and 3 others) were analyzed using 1H NMR spectroscopy. To evaluate the impact of body condition, diet and their interaction on each metabolite, proc GLIMMIX covariance structure was selected by the smallest Akaike information criterion value in SAS. The Shapiro-Wilk test was used to check normality and lognormal or beta distribution were applied when appropriate. Bonferroni correction post-hoc test was followed to correct Type I error. Statistical significance was considered at P < 0.05. In lean cats, significantly greater concentrations of fecal proteinogenic and branched-chain amino acids, such as methionine, valine, and L-lysine, as well as pyruvic acid and sugars (P < 0.05) were observed. This suggests that these metabolites may either be more actively fermented by bacteria or not utilized as extensively as in obese cats, potentially contributing to a reduction in obesity status. Regardless of body condition, there is a possibility of an enhanced activity in the tryptophan degradation pathway, leading to the greatest concentrations of fecal tryptophan (P = 0.04) in the LF diet, while the greatest fecal D-galactose (P = 0.04) in the LP diet was likely due to the greater dietary carbohydrate content. These observed results suggested that fecal metabolites were influenced by both body condition and diet; however, body condition had a more substantial impact on metabolite changes in cats, whereas no body condition and diet interaction were noted. Overall, the study indicated distinct fecal metabolic signatures in lean and obese cats subjected to different macronutrient diets and further research is warranted to validate the observed findings. Funding was provided by Champion Petfoods, Natural Sciences and Engineering Council of Canada and Mitacs Accelerate.

Data-driven optimal shrinkage of singular values under high-dimensional noise with separable covariance structure with application

We develop a data-driven optimal shrinkage algorithm, named extended OptShrink (eOptShrink), for matrix denoising with high-dimensional noise and a separable covariance structure. This noise is colored and dependent across samples. The algorithm leverages the asymptotic behavior of singular values and vectors of the noisy data's random matrix. Our theory includes the sticking property of non-outlier singular values, delocalization of weak signal singular vectors, and the spectral behavior of outlier singular values and vectors. We introduce three estimators: a novel rank estimator, an estimator for the spectral distribution of the pure noise matrix, and the optimal shrinker eOptShrink. Notably, eOptShrink does not require estimating the noise's separable covariance structure. We provide a theoretical guarantee for these estimators with a convergence rate. Through numerical simulations and comparisons with state-of-the-art optimal shrinkage algorithms, we demonstrate eOptShrink's application in extracting maternal and fetal electrocardiograms from single-channel trans-abdominal maternal electrocardiograms.

Structural brain characteristics of epilepsy patients with comorbid migraine without aura

Migraine is a common bi-directional comorbidity of epilepsy, indicating potential complex interactions between the two conditions. However, no previous studies have used brain morphology analysis to assess possible interactions between epilepsy and migraine. Voxel-based morphometry (VBM), surface-based morphometry (SBM), and structural covariance networks (SCNs) can be used to detect morphological changes with high accuracy. We recruited 30 individuals with epilepsy and comorbid migraine without aura (EM), along with 20 healthy controls (HC) and 30 epilepsy controls (EC) without migraine. We used VBM, SBM, and SCN analysis to compare differences in gray matter volume, cortical thickness, and global level and local level graph theory indexes between the EM, EC, and HC groups to investigate structural brain changes in the EM patients. VBM analysis showed that the EM group had gray matter atrophy in the right temporal pole compared with the HC group (p < 0.001, false discovery rate correction [FDR]). Furthermore, the headache duration in the EM group was negatively correlated with the gray matter volume of the right temporal pole (p < 0.05). SBM analysis showed cortical atrophy in the left insula, left posterior cingulate gyrus, left postcentral gyrus, left middle temporal gyrus, and left fusiform gyrus in the EM compared with the HC group (p < 0.001, family wise error correction). We found a positive correlation between headache frequency and the cortical thickness of the left middle temporal gyrus (p < 0.05). SCN analysis revealed no differences in global parameters between the three groups. The area under the curve (AUC) of the nodal betweenness centrality in the right postcentral gyrus was lower in the EM group compared with the HC group (p < 0.001, FDR correction), and the AUC of the nodal degree in the right fusiform gyrus was lower in the EM group compared with the EC group (p < 0.001, FDR correction). We found clear differences in brain structure in the EM patients compared with the HC group. Accordingly, migraine episodes may influence brain structure in epilepsy patients. Conversely, abnormal brain structure may be an important factor in the development of epilepsy with comorbid migraine without aura. Further studies are needed to investigate the role of brain structure in individuals with epilepsy and comorbid migraine without aura.

The Chronic Progressive Repeated Measures (CPRM) Model for Clinical Trials Comparing Change Over Time in Quantitative Trait Outcomes

Repeated measures analysis is a common analysis plan for clinical trials comparing change over time in quantitative trait outcomes in treatment versus control. Mixed model for repeated measures (MMRM) assuming an unstructured covariance of repeated measures is the default statistical analysis plan, with alternative covariance structures specified in the event that the MMRM model with unstructured covariance does not converge. We here describe a parsimonious covariance structure for repeated measures analysis that is specifically appropriate for longitudinal repeated measures of chronic progressive conditions. This model has the parsimonious features of the mixed effects model with random slopes and intercepts, but without restricting the repeated measure means to be linear with time. We demonstrate with data from completed trials that this pattern of longitudinal trajectories spreading apart over time is typical of Alzheimer’s disease. We further demonstrate that alternative covariance structures typically specified in statistical analysis plans using MMRM perform poorly for chronic progressive conditions, with the compound symmetry model being anticonservative, and the autoregressive model being poorly powered. Finally, we derive power calculation formulas for the chronic progressive repeated measures model that have the advantage of being independent of the design of the pilot studies informing the power calculations. When data follow the pattern of a chronic progressive condition. These power formulas are also appropriate for sizing clinical trials using MMRM analysis with unstructured covariance of repeated measures.

Effect of Dialysis on Structural Brain Connectivity in Patients with End-Stage Renal Disease

Introduction: Patients with end-stage renal disease (ESRD) are known to have reduced structural and functional brain connectivity in the brain regions associated with cognitive function. However, the effect of dialysis on brain connectivity remains unclear. This study aimed to evaluate the effects of dialysis on structural brain connectivity in patients with ESRD. Methods: This prospective study included 20 patients with ESRD in the pre-dialysis stage and 35 healthy controls. The patients underwent T2-weighted and three-dimensional T1-weighted magnetic resonance imaging before and 3 months after dialysis initiation. Moreover, the cortical thickness was calculated. We applied graph theoretical analysis to calculate the structural covariance network based on cortical thickness. We compared the cortical thickness and structural covariance network of patients with ESRD in the pre-dialysis stage with those of healthy controls and with those of patients with ESRD in the post-dialysis stage. Results: The mean cortical thickness in both hemispheres was lower in patients with ESRD in the pre-dialysis stage than in healthy controls (2.296 vs. 2.354, p = 0.030; 2.282 vs. 2.362, p = 0.004, respectively) and was higher in patients with ESRD in the post-dialysis stage than in those in the pre-dialysis stage (2.333 vs. 2.296, p = 0.001; 2.322 vs. 2.282, p = 0.002, respectively). Analysis of the structural covariance network revealed that the assortative coefficient was lower in patients with ESRD in the pre-dialysis stage than in healthy controls (−0.062 vs. −0.031, p = 0.029) and was higher in patients with ESRD in the post-dialysis stage than in those in the pre-dialysis stage (−0.002 vs. −0.062, p = 0.042). Conclusion: We observed differences in the cortical thickness and structural covariance networks before and after dialysis in patients with ESRD. This indicates that dialysis affects structural brain connectivity, contributing to the understanding of the pathophysiological mechanism of cognitive function alterations resulting from dialysis in patients with ESRD.

Concurrent Functional Linear Regression Via Plug-in Empirical Likelihood

AbstractFunctional data with non-smooth features (e.g., discontinuities in the functional mean and/or covariance) and monotonicity arise frequently in practice. This paper develops simultaneous inference for concurrent functional linear regression in this setting. We construct a simultaneous confidence band for a functional covariate effect of interest. Along with a Wald-type formulation, our approach is based on a powerful nonparametric likelihood ratio method. Our procedures are flexible enough to allow discontinuities in the coefficient functions and the covariance structure, while accounting for discretization of the observed trajectories under a fixed dense design. A simulation study shows that the proposed likelihood ratio-based procedure outperforms the Wald-type procedure in moderate sample sizes. We apply the proposed methods to studying the effect of age on the occupation time curve derived from wearable device data obtained in an NHANES study.

Progressive gray matter atrophy in parkinsonian variant of multiple system atrophy assessed by using causal structural covariance network.

Multiple system atrophy (MSA), a rare neurodegenerative disease, is usually accompanied by brain morphological alterations. However, the causal relationships between progressive gray matter atrophy in MSA parkinsonian (MSA-P) subtype remain unknown. In total, thirty-five MSA-P patients and thirty-five healthy controls (HC) underwent three-dimensional high-resolution T1-weighted structural imaging and voxel-based morphometry analysis. The causal structural covariance network (CaSCN) of gray matter was assessed to explore the causal relationships in MSA-P. With greater illness duration, the reduction of gray matter was originated from right cerebellum and progressed to bilateral cerebellum, fusiform gyrus, insula, putamen, caudate nucleus, frontal lobe, right angular gyrus, right precuneus, left middle occipital lobe and left inferior temporal lobe, then expanded to midbrain, bilateral para-hippocampus, thalamus, temporal lobe, inferior parietal lobule (IPL), precentral gyrus, postcentral gyrus and middle cingulate cortex. The right cerebellum was revealed to be the core node of the directional network and projected positive causal effects to bilateral cerebellum, caudate nucleus and left IPL. MSA-P patients showed progression of gray matter atrophy over time, with the right cerebellum probably as a primary hub. Furthermore, the early structural vulnerability of cerebellum in MSA-P may play a pivotal role in the modulation of motor and non-motor circuits at the structural level.

Structural covariance network activity in the medial prefrontal cortex is modulated by childhood abuse in adolescents with depression

Aberrant structural covariance (SC) in the medial prefrontal cortex (mPFC) is believed to play a crucial role in adolescent-onset major depressive disorder (AO-MDD). However, the effect of childhood abuse (CA) on SC in AO-MDD patients is still unknown. Here, we measured anomalous SC in the mPFC of AO-MDD patients and assessed the potential modulation of this feature by CA. We acquired T1-weighted structural images of AO-MDD patients (n = 93) and healthy controls (HCs, n = 81). Using voxel-based morphometry analysis, we calculated gray matter volumes for each subject. Subsequently, we classified abnormal SC in the mPFC into three subtypes according to overall CA. Compared with HCs, AO-MDD patients showed alterations in the structural covariance network of the mPFC, which is a central region in the default mode network (DMN). We also found an anterior-posterior dissociation in the structural covariance connectivity of the DMN. A history of CA modulated bilateral mPFC SC. These changes were primarily focused on the SC between the mPFC and the limbic system, indicating a gap in the rate of neural maturation between these regions. In summary, the DMN and frontal-limbic system, which are involved in emotional processing, appear to play a significant role in the development of AO-MDD. These findings highlight the crucial effects of CA on neurophysiological alterations in individuals with AO-MDD.

Structural covariation between cerebellum and neocortex intrinsic structural covariation links cerebellum subregions to the cerebral cortex.

The human cerebellum is increasingly recognized to be involved in nonmotor and higher-order cognitive functions. Yet, its ties with the entire cerebral cortex have not been holistically studied in a whole brain exploration with a unified analytical framework. Here, we characterized dissociable cortical-cerebellar structural covariation patterns based on regional gray matter volume (GMV) across the brain in n = 38,527 UK Biobank participants. Our results invigorate previous observations in that important shares of cortical-cerebellar structural covariation are described as 1) a dissociation between the higher-level cognitive system and lower-level sensorimotor system and 2) an anticorrelation between the visual-attention system and advanced associative networks within the cerebellum. We also discovered a novel pattern of ipsilateral, rather than contralateral, cerebral-cerebellar associations. Furthermore, phenome-wide association assays revealed key phenotypes, including cognitive phenotypes, lifestyle, physical properties, and blood assays, associated with each decomposed covariation pattern, helping to understand their real-world implications. This systems neuroscience view paves the way for future studies to explore the implications of these structural covariations, potentially illuminating new pathways in our understanding of neurological and cognitive disorders.NEW & NOTEWORTHY Cerebellum's association with the entire cerebral cortex has not been holistically studied in a unified way. Here, we conjointly characterize the population-level cortical-cerebellar structural covariation patterns leveraging ∼40,000 UK Biobank participants whole brain structural scans and ∼1,000 phenotypes. We revitalize the previous hypothesis of an anticorrelation between the visual-attention system and advanced associative networks within the cerebellum. We also discovered a novel ipsilateral cerebral-cerebellar associations. Phenome-wide association (PheWAS) revealed real-world implications of the structural covariation patterns.

Spatial Patterns of Productivity and Human Development Potentials for Pinus pinea L.

Pinus pinea (stone pine), a Mediterranean species, is valued for its highly nutritious pine nuts and its ability to adapt to different environmental conditions. The species has been increasingly planted in Chile, where its main ecological requirements are met across a vast area. However, new plantations are established without considering social dimensions. Policymakers can regulate private decisions on tree planting through the appropriate design of economic incentives to foster social well-being. The objective of this work was to describe spatial patterns of potential areas for the cultivation of the exotic nut-bearing conifer P. pinea in central Chile and the possible correlation of those patterns with human development indices. Spatial data layers of the municipality development index (MDI), elevation, edaphoclimatic variables, and stone pine nut’s productive potential were overlapped at the municipality scale along 1225 km in central Chile. A spatial principal component analysis (sPCA) was used to integrate multiple dimensions, summarizing covariation structures, and identifying spatial patterns in the study area. Key results showed that spatial patterns of the potential productive index (PPI) were strongly regulated by the spatial pattern of climate and soil variables, whereas the spatial pattern of MDI showed a cryptic pattern and that the three dimensions of MDI—welfare, economy, and education—showed a different spatial movement, especially education and welfare. The results allow us to recommend that public policies boost municipality development through the promotion of P. pinea plantations and should target areas with a high productive potential and low MDI to generate socio-economic improvements. These findings are useful for the strategic spatial planning of the species cropping in Chile.

Neural correlates of anhedonia in young adults with subthreshold depression: A graph theory approach for cortical-subcortical structural covariance

BackgroundAnhedonia is an enduring symptom of subthreshold depression (StD) and predict later onset of major depressive disorder (MDD). Brain structural covariance describes the inter-regional distribution of morphological changes compared to healthy controls (HC) and reflects brain maturation and disease progression. We investigated neural correlates of anhedonia from the structural covariance. MethodsT1-weighted brain magnetic resonance images were acquired from 79 young adults (26 StD, 30 MDD, and 23 HC). Intra-individual structural covariance networks of 68 cortical surface area (CSAs), 68 cortical thicknesses (CTs), and 14 subcortical volumes were constructed. Group-level hubs and principal edges were defined using the global and regional graph metrics, compared between groups, and examined for the association with anhedonia severity. ResultsGlobal network metrics were comparable among the StD, MDD, and HC. StD exhibited lower centralities of left pallidal volume than HC. StD showed higher centralities than HC in the CSAs of right rostral anterior cingulate cortex (ACC) and pars triangularis, and in the CT of left pars orbitalis. Less anhedonia was associated with higher centralities of left pallidum and right amygdala, higher edge betweenness centralities in the structural covariance (EBSC) of left postcentral gyrus-parahippocampal gyrus and LIPL-right amygdala. More anhedonia was associated with higher centralities of left inferior parietal lobule (LIPL), left postcentral gyrus, left caudal ACC, and higher EBSC of LIPL-left postcentral gyrus, LIPL-right lateral occipital gyrus, and left caudal ACC-parahippocampal gyrus. LimitationsThis study has a cross-sectional design. ConclusionsStructural covariance of brain morphologies within the salience and limbic networks, and among the salience-limbic-default mode-somatomotor-visual networks, are possible neural correlates of anhedonia in depression.

Changed ventral striatum structural covariance and grey matter volume in depression during a one-year follow-up

Empirical findings suggest reduced cortico-striatal structural connectivity in patients with major depressive disorder (MDD). However, the relationship between the abnormal structural covariance and one-year outcome of first-episode drug-naive patients has not been evaluated. This longitudinal study aimed to identify specific changes of ventral striatum-related brain structural covariance and grey matter volume in forty-two first-episode patients with major depression disorder compared with thirty-seven healthy controls at the baseline and the one-year follow-up conditions. At the baseline, patients showed decreased structural covariance between the left ventral striatum and the bilateral superior frontal gyrus (SFG), bilateral middle frontal gyrus (MFG), right supplementary motor area (SMA) and left precentral gyrus and increased grey matter volume at the left fusiform and left parahippocampus. At the one-year follow-up, patients showed decreased structural covariance between the left ventral striatum and the right SFG, right MFG, left precentral gyrus and left postcentral gyrus, and increased structural covariance between the right ventral striatum and the right amygdala, right hippocampus, right parahippocampus, right superior temporal pole, right insula and right olfactory bulb and decreased volume at the left SMA compared with controls. These findings suggest that specific ventral striatum connectivity changes contribute to the early brain development of the MDD.

Decision tree learning with random forest models using agricultural and ecological field data incorporating multi-factor studies and covariate structure

Decision tree learning with random forest models using agricultural and ecological field data incorporating multi-factor studies and covariate structure

The Bergen-Yale Sexual Addiction Scale (BYSAS): Longitudinal Measurement Invariance Across a Two-Year Interval.

The Bergen-Yale Sexual Addiction Scale (BYSAS; [1]) is arguably the most popular questionnaire at present for assessing sex addiction. Employing Confirmatory Factor Analysis (CFA) and treating item scores as ordered categorical, we applied Weighted Least Square Mean and Variance Adjusted Chi-Square (WLSMV) extraction to investigate the longitudinal measurement and structural invariance of ratings on the BYSAS among 276 adults (mean = 31.86 years; SD = 9.94 years; 71% male) over a two-year period, with ratings at three yearly intervals. Overall, there was support for configural invariance, full loading, full threshold, the full unique factor invariance; and all structural (latent variances and covariances) components. Additionally, there was no difference in latent mean scores across the three-time points. The psychometric and practical implications of the findings are discussed.

An optimal Bayesian strategy for comparing Wiener–Hunt deconvolution models in the absence of ground truth

Abstract This paper considers the quantitative comparison of several alternative models to perform deconvolution in situations where there is no ground truth data available. With applications to very large data sets in mind, we focus on linear deconvolution models based on a Wiener filter. Although comparatively simple, such models are widely prevalent in large scale setting such as high-resolution image restoration because they provide an excellent trade-off between accuracy and computational effort. However, in order to deliver accurate solutions, the models need to be properly calibrated in order to capture the covariance structure of the unknown quantity of interest and of the measurement error. This calibration often requires onerous controlled experiments and extensive expert supervision, as well as regular recalibration procedures. This paper adopts an unsupervised Bayesian statistical approach to model assessment that allows comparing alternative models by using only the observed data, without the need for ground truth data or controlled experiments. Accordingly, the models are quantitatively compared based on their posterior probabilities given the data, which are derived from the marginal likelihoods or evidences of the models. The computation of these evidences is highly non-trivial and this paper consider three different strategies to address this difficulty—a Chib approach, Laplace approximations, and a truncated harmonic expectation—all of which efficiently implemented by using a Gibbs sampling algorithm specialised for this class of models. In addition to enabling unsupervised model selection, the output of the Gibbs sampler can also be used to automatically estimate unknown model parameters such as the variance of the measurement error and the power of the unknown quantity of interest. The proposed strategies are demonstrated on a range of image deconvolution problems, where they are used to compare different modelling choices for the instrument’s point spread function and covariance matrices for the unknown image and for the measurement error.

Gray matter structural alterations of cortico-striato-thalamo-cortical loop in familial Paroxysmal Kinesigenic Dyskinesia

BackgroundPrevious studies have indicated that patients with Paroxysmal Kinesigenic Dyskinesia (PKD) exhibit reduced gray matter volume in certain brain regions within the cortico-striato-thalamo-cortical (CSTC) loop. However, a comprehensive investigation specifically targeting the CSTC loop in PKD has never been conducted. ObjectivesTo provide evidence for the involvement of the CSTC loop in the pathogenesis of PKD from the perspective of structural alterations, this study carried out a surface-based morphometry (SBM), voxel-based morphometry (VBM), and structural covariance networks (SCN) combined analysis in familial PKD patients. MethodsA total of 8 familial PKD patients and 10 healthy family members were included in the study and underwent Brain MRI examinations. Based on 3D T1 MPRAGE data, neuroimaging metrics of cortical thickness from SBM, subcortical nuclei volume from VBM, and covariance coefficient from SCN were used to systematically investigate the brain structural alterations along the CSTC loop of PKD patients. ResultsA significant decrease in the average cortical thickness of the left S1 region in the PKD group was observed. The volumes of subcortical nuclei, including the thalamus, putamen, and globus pallidus were reduced, with a pronounced effect observed in the bilateral putamen. And the structural covariance connection between the left putamen and the left globus pallidus was significantly strengthened. ConclusionsThe study confirms the involvement of the CSTC loop in the pathogenesis of PKD from the perspective of structural alterations, and the findings may provide potential targets for objective diagnosis and therapeutic monitoring of PKD.

Population Pharmacokinetic-Pharmacodynamic Analysis of a Reserpine-Induced Myalgia Model in Rats.

(1) Background: Fibromyalgia syndrome (FMS) is a chronic pain condition with widespread pain and multiple comorbidities, for which conventional therapies offer limited benefits. The reserpine-induced myalgia (RIM) model is an efficient animal model of FMS in rodents. This study aimed to develop a pharmacokinetic-pharmacodynamic (PK-PD) model of reserpine in rats, linking to its impact on monoamines (MAs). (2) Methods: Reserpine was administered daily for three consecutive days at dose levels of 0.1, 0.5, and 1 mg/kg. A total of 120 rats were included, and 120 PK and 828 PD observations were collected from 48 to 96 h after the first dose of reserpine. Non-linear mixed-effect data analysis was applied for structural PK-PD model definition, variability characterization, and covariate analysis. (3) Results: A one-compartment model best described reserpine in rats (V = 1.3 mL/kg and CL = 4.5 × 10-1 mL/h/kg). A precursor-pool PK-PD model (kin = 6.1 × 10-3 mg/h, kp = 8.6 × 10-4 h-1 and kout = 2.7 × 10-2 h-1) with a parallel transit chain (k0 = 1.9 × 10-1 h-1) characterized the longitudinal levels of MA in the prefrontal cortex, spinal cord, and amygdala in rats. Reserpine stimulates the degradation of MA from the pool compartment (Slope1 = 1.1 × 10-1 h) and the elimination of MA (Slope2 = 1.25 h) through the transit chain. Regarding the reference dose (1 mg/kg) of the RIM model, the administration of 4 mg/kg would lead to a mean reduction of 65% (Cmax), 80% (Cmin), and 70% (AUC) of MA across the brain regions tested. (4) Conclusions: Regional brain variations in neurotransmitter depletion were identified, particularly in the amygdala, offering insights for therapeutic strategies and biomarker identification in FMS research.

Hilbert space valued Gaussian processes, their kernels, factorizations, and covariance structure

Hilbert space valued Gaussian processes, their kernels, factorizations, and covariance structure

Decorrelated nearest shrunken centroids for tensor data

AbstractThe nearest shrunken centroids (NSC) method is an efficient and accurate classifier. However, it is incapable of modelling correlation among predictors. Moreover, many contemporary datasets have tensor predictors that cannot be directly handled by NSC. We tackle these challenges by proposing a new distance‐based classifier, tensor decorrelated NSC (TDNSC). TDNSC leverages the popular separable covariance structure on tensor data to decorrelate data and allow easy application of NSC afterwards. Unlike existing tensor classifiers that often rely on complicated iterative algorithms, TDNSC has analytical solutions. The theoretical properties and empirical results suggest that TDNSC is a promising method for tensor classification.