Effect of Growing Regions on Discrimination of Turkish-Style Black Table Olives from Gemlik Cultivar.

Gemlik is a cultivar that grows in a distinct region of Turkiye and is ideal for brine fermentation of brine black table olives. Bursa Protected Designated Origin (PDO) and Izmir non-PDO Gemlik table olives have high levels of oleic acid (74%), total phenol (190 mg/kg), and dry matter (57%), while being low in linoleic acid (8%). The pH values and salt contents were observed to be in the range of 4.1 to 4.3 and 3.9% to 4.8%, respectively. During the fermentation of Gemlik table olives, a mass transfer occurred, resulting in a reduction in reducing sugar and total sugar contents as well as an increase in the salt content of the olives. Despite the reduction of phenolic content in both Gemlik PDO and non-PDO table olives, their antioxidant capacity remains high after fermentation. The oil content, antioxidant activity, phenolic contents, palmitic, palmitoleic, oleic, and linoleic acids were all found to be significant variables in distinguishing between Gemlik PDO and non-PDO table olives using PLS-DA analysis. There is a statistically significant correlation between the phenolic content and oleic (0.588) and linoleic (-0.659) acids (p < 0.05). Bursa PDO and Izmir non-PDO exhibit enhanced nutritional quality and antioxidant activity, unequivocally differentiating them from Hatay and Mersin non-PDO Gemlik table olives with 98% accuracy through discriminant analysis (p < 0.05). PLS-DA and DA can effectively identify variations in the quality of Turkish-style black table olives preserved in brine, originating from PDO and non-PDO growing areas.

Mobile Laser-Induced Breakdown Spectroscopy for Future Application in Precision Agriculture-A Case Study.

In precision agriculture, the estimation of soil parameters via sensors and the creation of nutrient maps are a prerequisite for farmers to take targeted measures such as spatially resolved fertilization. In this work, 68 soil samples uniformly distributed over a field near Bonn are investigated using laser-induced breakdown spectroscopy (LIBS). These investigations include the determination of the total contents of macro- and micronutrients as well as further soil parameters such as soil pH, soil organic matter (SOM) content, and soil texture. The applied LIBS instruments are a handheld and a platform spectrometer, which potentially allows for the single-point measurement and scanning of whole fields, respectively. Their results are compared with a high-resolution lab spectrometer. The prediction of soil parameters was based on multivariate methods. Different feature selection methods and regression methods like PLS, PCR, SVM, Lasso, and Gaussian processes were tested and compared. While good predictions were obtained for Ca, Mg, P, Mn, Cu, and silt content, excellent predictions were obtained for K, Fe, and clay content. The comparison of the three different spectrometers showed that although the lab spectrometer gives the best results, measurements with both field spectrometers also yield good results. This allows for a method transfer to the in-field measurements.

Univariate versus multivariate spectrophotometric data analysis of triamterene and xipamide; a quantitative and qualitative greenly profiled comparative study.

Triamterene (TRI) and xipamide (XIP) mixture is used as a binary medication of antihypertension which is considered as a major cause of premature death worldwide. The purpose of this research is the quantitative and qualitative analysis of this binary mixture by green univariate and multivariate spectrophotometric methods. Univariate methods were zero order absorption spectra method (D0) and Fourier self-deconvolution (FSD), as TRI was directly determined by D0 at 367.0 nm in the range (2.00-10.00 µg/mL), where XIP show no interference. While XIP was determined by FSD at 261.0 nm in the range (2.00-8.00 µg/mL), where TRI show zero crossing. Multivariate methods were Partial Least Squares, Principal Component Regression, Artificial Neural Networks, and Multivariate Curve Resolution-Alternating Least Squares. A training set of 25 mixtures with different quantities of the tested components was used to construct and evaluate them, 3 latent variables were displayed using an experimental design. A set of 18 synthetic mixtures with concentrations ranging from (3.00-7.00 µg/mL) for TRI and (2.00-6.00 µg/mL) for XIP, were used to construct the calibration models. A collection of seven synthetic mixtures with various quantities was applied to build the validation models. All the proposed approaches quantitative analyses were evaluated using recoveries as a percentage, root mean square error of prediction, and standard error of prediction. Strong multivariate statistical tools were presented by these models, and they were used to analyze the combined dosage form available on the Egyptian market. The proposed techniques were evaluated in accordance with ICH recommendations, where they are capable of overcoming challenges including spectral overlaps and collinearity. When the suggested approaches and the published one were statistically compared, there was no discernible difference between them. The green analytical method index and eco-scale tools were applied for assessment of the established models greenness. The suggested techniques can be used in product testing laboratories for standard pharmaceutical analysis of the substances being studied.

Novel multivariate methods to track frequency shifts of neural oscillations in EEG/MEG recordings.

Instantaneous and peak frequency changes in neural oscillations have been linked to many perceptual, motor, and cognitive processes. Yet, the majority of such studies have been performed in sensor space and only occasionally in source space. Furthermore, both terms have been used interchangeably in the literature, although they do not reflect the same aspect of neural oscillations. In this paper, we discuss the relation between instantaneous frequency, peak frequency, and local frequency, the latter also known as spectral centroid. Furthermore, we propose and validate three different methods to extract source signals from multichannel data whose (instantaneous, local, or peak) frequency estimate is maximally correlated to an experimental variable of interest. Results show that the local frequency might be a better estimate of frequency variability than instantaneous frequency under conditions with low signal-to-noise ratio. Additionally, the source separation methods based on local and peak frequency estimates, called LFD and PFD respectively, provide more stable estimates than the decomposition based on instantaneous frequency. In particular, LFD and PFD are able to recover the sources of interest in simulations performed with a realistic head model, providing higher correlations with an experimental variable than multiple linear regression. Finally, we also tested all decomposition methods on real EEG data from a steady-state visual evoked potential paradigm and show that the recovered sources are located in areas similar to those previously reported in other studies, thus providing further validation of the proposed methods.

Enhancement of EEG-EMG coupling detection using corticomuscular coherence with spatial-temporal optimization.

Objective. Corticomuscular coherence (CMC) is widely used to detect and quantify the coupling between motor cortex and effector muscles. It is promisingly used in human-machine interaction (HMI) supported rehabilitation training to promote the closed-loop motor control for stroke patients. However, suffering from weak coherence features and low accuracy in contingent neurofeedback, its application to HMI rehabilitation robots is currently limited. In this paper, we propose the concept of spatial-temporal CMC (STCMC), which is the coherence by refining CMC with delay compensation and spatial optimization.Approach. The proposed STCMC method measures the coherence between electroencephalogram (EEG) and electromyogram (EMG) in the multivariate spaces. Specifically, we combined delay compensation and spatial optimization to maximize the absolute value of the coherence. Then, we tested the reliability and effectiveness of STCMC on neurophysiological data of force tracking tasks.Main results. Compared with CMC, STCMC not only enhanced the coherence significantly between brain and muscle signals, but also produced higher classification accuracy. Further analysis showed that temporal and spatial parameters estimated by the STCMC reflected more detailed brain topographical patterns, which emphasized the different roles between the contralateral and ipsilateral hemisphere.Significance. This study integrates delay compensation and spatial optimization to give a new perspective for corticomuscular coupling analysis. It is also feasible to design robotic neurorehabilitation paradigms by the proposed method.

Quantitative analyses of cytokine profiles reveal hormone-mediated modulation of cytokine profiles in recurrent spontaneous miscarriage.

PURPOSE: Cytokines play important roles in pregnancy complications. Some hormones such as estrogen, progesterone, and dydrogesterone have been shown to alter cytokine profiles. Understanding how cytokine profiles are affected by these hormones is therefore an important step towards immunomodulatory therapies for pregnancy complications. We analyse previously published data on the effects of estrogen, progesterone, and dydrogesterone on cytokine balances in women having recurrent spontaneous miscarriages. MATERIALS AND METHODS: Levels of eight cytokines (IFN-γ, IL-2, IL-6, IL-10, IL-13, IL-17, IL-23, TNF-α) from n = 22 women presenting unexplained recurrent spontaneous miscarriages were studied. Cytokine values were recorded after in vitro exposure of peripheral blood cells to estrogen, progesterone, and dydrogesterone. We expand on earlier analysis of the dataset by employing different statistical techniques including effect sizes for individual cytokine values, a more powerful statistical test, and adjusting p-values for multiple comparisons. We employ multivariate analysis methods, including to determine the relative magnitude of the effects of the hormone therapies on cytokines. A new statistical method is introduced based on pairwise distances able to accommodate complex relations in cytokine profiles. RESULTS: We report several statistically significant differences in individual cytokine values between the control group and each hormone treated group, with estrogen affecting the fewest cytokines, and progesterone and dydrogesterone both affecting seven out of eight cytokines. Exposure to estrogen produces no large effects sizes however, while IFN-γ and IL-17 show large effect sizes for both progesterone and dydrogesterone, among other cytokines. Our new method for identifying which collections (i.e. subsets) of cytokines best distinguish contrasting groups identifies IFN-γ, IL-10 and IL-23 as especially noteworthy for both progesterone and dydrogesterone treatments. CONCLUSIONS: While some statistically significant differences in cytokine levels after exposure to estrogen are found, these have small effect sizes and are unlikely to be clinically relevant. Progesterone and dydrogesterone both induce statistically significant and large effect-size differences in cytokine levels, hence therapy with these two progestogens is more likely to be clinically relevant. Univariate and multivariate methods for identifying cytokine importances provide insight into which groups of cytokines are most affected and in what ways by therapies.

multiSyncPy: A Python package for assessing multivariate coordination dynamics.

In order to support the burgeoning field of research into intra- and interpersonal synchrony, we present an open-source software package: multiSyncPy. Multivariate synchrony goes beyond the bivariate case and can be useful for quantifying how groups, teams, and families coordinate their behaviors, or estimating the degree to which multiple modalities from an individual become synchronized. Our package includes state-of-the-art multivariate methods including symbolic entropy, multidimensional recurrence quantification analysis, coherence (with an additional sum-normalized modification), the cluster-phase 'Rho' metric, and a statistical test based on the Kuramoto order parameter. We also include functions for two surrogation techniques to compare the observed coordination dynamics with chance levels and a windowing function to examine time-varying coordination for most of the measures. Taken together, our collation and presentation of these methods make the study of interpersonal synchronization and coordination dynamics applicable to larger, more complex and often more ecologically valid study designs. In this work, we summarize the relevant theoretical background and present illustrative practical examples, lessons learned, as well as guidance for the usage of our package - using synthetic as well as empirical data. Furthermore, we provide a discussion of our work and software and outline interesting further directions and perspectives. multiSyncPy is freely available under the LGPL license at: https://github.com/cslab-hub/multiSyncPy , and also available at the Python package index.

Screening of neoplastic diseases by statistical analysis of urine fluorescence spectroscopic data. Application of multivariate techniques for enhancing classification.

The composition of human fluids is modified during the course of neoplastic diseases. Urine analysis offers the advantage of being a noninvasive method for which samples are easily and routinely collected from patients. In this work, urine fluorescence spectra recorded upon excitation at 405 nm were obtained from healthy volunteers and individuals with different oncologic pathologies. A large number of indexes, i.e., parameters obtained from spectral data which assist spectral features characterization, were developed to classify healthy and pathological populations. The discrimination ability of simple predictive indexes, obtained from spectra pretreated with different normalization procedures and by taking their derivatives, was statistically assessed. In addition, multivariate methods, such as principal component analysis and multivariate curve resolution by alternating least squares, were used to develop more elaborate indexes for distinguishing between healthy and pathological populations. All indexes were systematically evaluated on a statistical basis by in lab-developed routines capable of detecting outliers, judging the normal distribution of the indexes, evaluating variance homogeneity, testing the difference between the means of healthy and pathological populations, as well as performing a receiver operator curve analysis to assess the classification power of each index. Those indexes with the best performances were further combined to perform a linear discriminant analysis, which yielded a powerful classification algorithm with an area under the receiver operator curve of 0.986, a sensitivity of 97.7%, a specificity of 100%, and an overall accuracy of 98.8%. The present study shows that the statistical analysis of urine fluorescence data with a proper combination of multivariate techniques bears a high potential to develop massive screening tests for the early detection of oncologic pathologies.

Uni- and multivariate bias adjustment methods in Nordic catchments: Complexity and performance in a changing climate.

For climate-change impact studies at the catchment scale, meteorological variables are typically extracted from ensemble simulations provided by global and regional climate models, which are then downscaled and bias-adjusted for each study site. For bias adjustment, different statistical methods that re-scale climate model outputs have been suggested in the scientific literature. They range from simple univariate methods that adjust each meteorological variable individually, to more complex and more demanding multivariate methods that take existing relationships between meteorological variables into consideration. Over the past decade, several attempts have been made to evaluate such methods in various regions. There is, however, still no guidance for choosing appropriate bias adjustment methods for a study at hand. In particular, the question whether the benefits of potentially improved adjustments outweigh the cost of increased complexity, remains unanswered. This paper presents a comprehensive evaluation of the performance of two commonly used univariate and two multivariate bias adjustment methods in reproducing numerous univariate, multivariate and temporal features of precipitation and temperature series in different catchments in Sweden. The paper culminates in a discussion on trade-offs between the potential benefits (i.e., skills and added value) and disadvantages (complexity and computational demand) of each method to offer plausible, defensible and actionable insights from the standpoint of climate-change impact studies in high latitudes. We concluded that all selected bias adjustment methods generally improved the raw climate model simulations, but that not a single method consistently outperformed the other methods. There were, however, differences in the methods' performance for particular statistical features, indicating that other practical aspects such as computational time and heavy theoretical requirements should also be taken into consideration when choosing an appropriate bias adjustment method.

Biochemical changes of macrophages and U87MG cells occurring as a result of the exposure to iron oxide nanoparticles detected with the Raman microspectroscopy.

The core size of iron oxide nanoparticles (IONPs) is a crucial factor defining not only their magnetic properties but also toxicological profile and biocompatibility. On the other hand, particular IONPs may induce different biological response depending on the dose, exposure time, but mainly depending on the examined system. New light on this problem may be shed by the information concerning biomolecular anomalies appearing in various cell lines in response to the action of IONPs with different core diameters and this was accomplished in the present study. Using Raman microscopy we studied the abnormalities in the accumulation of proteins, lipids and organic matter within the nucleus, cytoplasm and cellular membrane of macrophages, HEK293T and U87MG cell line occurring as a result of 24-hour long exposure to PEG-coated magnetite IONPs. The examined nanoparticles had 5, 10 and 30 nm cores and were administered in doses 5 and 25 µg Fe/ml. The obtained results showed significant anomalies in biochemical composition of macrophages and the U87MG cells, but not the HEK293T cells, occurring as a result of exposure to all of the examined nanoparticles. However, IONPs with 10 nm core diminished the accumulation of biomolecules in cells only when they were administered at a larger dose. The Raman spectra recorded for the macrophages subjected to 30 nm IONPs and for the U87MG cells exposed to 5 and 10 nm showed the presence of additional bands in the wavenumber range 1700-2400 cm-1, probably resulting from the appearance of Fe adducts within cells. Our results indicate, moreover, that smaller IONPs may be effectively internalized into the U87MG cells, which points at their diagnostic/therapeutic potential in the case of glioblastoma multiforme.

Multivariate genome-wide association study models to improve prediction of Crohn's disease risk and identification of potential novel variants.

BACKGROUND: Crohn's disease (CD) is a type of inflammatory bowel disease (IBD) that affects the gastrointestinal tract with diverse symptoms. At present, genome-wide association studies (GWAS) has discovered more than 140 genetic loci associated with CD from several datasets. Using the usual univariate GWAS methods, researchers have discovered common variants with small effects. Univariate methods assume independence among the variants that miss subtle combinatorial signals. Multivariate approaches have improved risk prediction and have complemented univariate methods for elucidating the etiology of complex traits and potential novel associations. However, the current multivariate models for CD have been assessed for three datasets (published from 2006 to 2008) under unrelated methodological settings showing a broad performance spectrum. Notably, these multivariate studies do not analyze potential novel variants. Here, we aimed to perform a robust multivariate analysis of a CD dataset different from the one commonly used, and we used the information yielded by the models to identify whether the generated models could provide additional information about the potential novel variants of CD. METHODS: Therefore, we compared different multivariate methods and models, LASSO (least absolute shrinkage and selection operator), XGBoost, random forest (RF), Bootstrap stage-wise model selection (BSWiMS), and LDpred, using a strict random subsampling approach to predict the CD risk using a recent GWAS dataset, United Kingdom IBD IBD Genetics Consortium (UKIBDGC), made available in 2017, that had not been used for CD prediction studies. In addition, we assessed the effect of common strategies by increasing and decreasing the number of single-nucleotide polymorphism (SNP) markers (using genotype imputation and linkage disequilibrium (LD)-clumping). RESULTS: We found that the LDpred model without any imputation was the best model among all the tested models for predicting the CD risk (area under the receiver operating characteristic curve (AUROC) = 0.667 ± 0.024) in this dataset. We validated the best models using a second dataset (National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK) IBD Genetics Consortium, which was previously used in CD prediction studies) in which LDpred was also the best method with a similar performance (AUROC = 0.634 ± 0.009). Based on the importance of the variants yielded by the multivariate models, we identified an unnoticed region within chromosome 6, tagged by SNP rs4945943; this region was close to the gene MARCKS, which appeared to contribute to CD risk. CONCLUSIONS: This research is the first multivariate prediction analysis applied to the UKIBDGC dataset. Our robust multivariate setting analysis enabled us to identify a potential variant that contributed to the CD risk. Multivariate methods are valuable tools for identifying genes that contribute to disease risk.

Spatial covariance analysis of FDG-PET and HMPAO-SPECT for the differential diagnosis of dementia with Lewy bodies and Alzheimer's disease.

We investigated diagnostic characteristics of spatial covariance analysis (SCA) of FDG-PET and HMPAO-SPECT scans in the differential diagnosis of dementia with Lewy bodies (DLB) and Alzheimer's disease (AD), in comparison with visual ratings and region of interest (ROI) analysis. Sixty-seven patients (DLB 29, AD 38) had both HMPAO-SPECT and FDG-PET scans. Spatial covariance patterns were used to separate AD and DLB in an initial derivation group (DLB n=15, AD n=19), before being forward applied to an independent group (DLB n=14, AD n=19). Visual ratings were by consensus, with ROI analysis utilising medial occipital/medial temporal uptake ratios. SCA of HMPAO-SPECT performed poorly (AUC 0.59±0.10), whilst SCA of FDG-PET (AUC 0.83±0.07) was significantly better. For FDG-PET, SCA showed similar diagnostic performance to ROI analysis (AUC 0.84±0.08) and visual rating (AUC 0.82±0.08). In contrast to ROI analysis, there was little concordance between SCA and visual ratings of FDG-PET scans. We conclude that SCA of FDG-PET outperforms that of HMPAO-SPECT. SCA of FDG-PET also performed similarly to the other analytical approaches, despite the limitations of a relatively small SCA derivation group. Compared to visual rating, SCA of FDG-PET relies on different sources of group variance to separate DLB from AD.

Multivariate optimization results in an edible extract from Ilex paraguariensis unexplored residues with a high amount of phenolic compounds.

Ilex paraguariensis A. St. Hil. plants are used for the preparation of food and drinks which are widely consumed worldwide. During the harvest season of these plants, 2-5 ton hec-1 of agricultural residue is generated, which remains underutilized. Therefore, this study aimed to obtain an edible extract with high content of bioactive compounds and antimicrobial properties from the agricultural residue of I. paraguariensis for industrial use in food applications. The extraction conditions were optimized through a multivariate experimental design using ethanol:water. The extracted compounds were characterized by HPLC-ESY-QTOF-MS. In the optimal extraction conditions, 55 compounds were extracted, including 8 compounds that were not previously reported in I. paraguariensis. The method proved to be simple, fast, economical and environmentally friendly, with the use of green solvents. This optimization allowed for the extraction of 15.07 g of phenolic compounds per 100 g of residue. The extract showed high antioxidant activity and the capacity to inhibit Staphylococcus aureus. Results indicate that it is possible to obtain an edible extract with a high content of bioactive compounds, particularly phenolic compounds, from the I. paraguariensis residue, which has high prospects for the valorization of unexplored natural resources.

Chemical and structural aspects of fresh and fossil marine mollusc shells investigated by mid-infrared and near-infrared spectroscopy with the support of statistical and multivariate methods.

In the present study, we applied Fourier transform infrared (FTIR) and Fourier transform near infrared (FTNIR) spectroscopy to investigate some specific structural aspects of Patella caerulea, Mytilus edulis, Ostrea edulis, and Calista chione shells sampled in different sites. Moreover, for Ostrea edulis and Calista chione, the present study also included fossil samples. As far as FTIR spectroscopy is concerned, the support of statistical and multivariate methods such as the average spectrum (AV), spectral deconvolution, and two-dimensional correlation analysis (2DCOS) allowed to detect structural differences existing within the same mollusc species as a function of the sites they come. These differences can be reasonably linked to the local environmental conditions, which affect the biomineralization pattern of shell formation and growth. These structural differences are related to the calcite, aragonite, Mg-calcite contents, and interactions, as presently observed for fresh and fossil shells. The application of 2DCOS and deconvolution to FTIR spectra also showed the role of the amorphous calcium carbonate (ACC) in the structural characterization of shells, then suggesting the use of a new parameter, the calcite and aragonite to ACC (CAACC) ratio, as a new measurement for the structural characterization of shells. At last, FTNIR spectroscopy allowed detecting the presence of α-helix and ß-sheet protein structures in the shells. The results of this study show that also FTIR and FTNIR spectroscopy are able to discern differences in structural characteristics of mollusc shells, a field of environmental studies where scanning electron microscopy and X-ray diffraction are the more widely used methods.

A hierarchical Bayesian model to find brain-behaviour associations in incomplete data sets.

Canonical Correlation Analysis (CCA) and its regularised versions have been widely used in the neuroimaging community to uncover multivariate associations between two data modalities (e.g., brain imaging and behaviour). However, these methods have inherent limitations: (1) statistical inferences about the associations are often not robust; (2) the associations within each data modality are not modelled; (3) missing values need to be imputed or removed. Group Factor Analysis (GFA) is a hierarchical model that addresses the first two limitations by providing Bayesian inference and modelling modality-specific associations. Here, we propose an extension of GFA that handles missing data, and highlight that GFA can be used as a predictive model. We applied GFA to synthetic and real data consisting of brain connectivity and non-imaging measures from the Human Connectome Project (HCP). In synthetic data, GFA uncovered the underlying shared and specific factors and predicted correctly the non-observed data modalities in complete and incomplete data sets. In the HCP data, we identified four relevant shared factors, capturing associations between mood, alcohol and drug use, cognition, demographics and psychopathological measures and the default mode, frontoparietal control, dorsal and ventral networks and insula, as well as two factors describing associations within brain connectivity. In addition, GFA predicted a set of non-imaging measures from brain connectivity. These findings were consistent in complete and incomplete data sets, and replicated previous findings in the literature. GFA is a promising tool that can be used to uncover associations between and within multiple data modalities in benchmark datasets (such as, HCP), and easily extended to more complex models to solve more challenging tasks.

Meta-Analysis Methods of Diagnostic Test Accuracy Studies.

Meta-analytic techniques are used to combine the results of different studies that have evaluated the accuracy of diagnostic tests. In this article, we present univariate and multivariate meta-analysis methods for a single test and we provide an extensive description of methods for meta-analysis and comparison of multiple diagnostic tests. We close with a practical example of a meta-analysis that aimed to determine whether Rheumatoid Factor identifies patients with Rheumatoid Arthritis.

Dataset of functional connectivity during cognitive control for an adult lifespan sample.

We provide functional connectivity matrices generated during functional magnetic resonance imaging (fMRI) during different tasks of cognitive control in healthy aging adults. These data can be used to replicate the primary results from the related manuscript: Reconfiguration and dedifferentiation of functional networks during cognitive control across the adult lifespan (Rieck et al., 2021). One-hundred-forty-four participants (ages 20-86) were scanned on a Siemens 3T MRI scanner while they were completing tasks to measure functional activity during inhibition, initiation, shifting, and working memory. Estimates of functional connectivity (quantified with timeseries correlations) between different brain regions were computed using three different brain atlases: Schaefer 100 parcel 17 network atlas (Schaefer et al., 2018; Yeo et al., 2011), Power 229 node 10 network atlas (Power et al., 2011), and Schaefer 200 parcel 17 network atlas (Schaefer et al., 2018; Yeo et al., 2011). The resulting functional connectivity correlation matrices are provided as text files with this article. Cov-STATIS (Abdi et al., 2012; a multi-table multivariate statistical technique; https://github.com/HerveAbdi/DistatisR) was used to examine similarity between functional connectivity during the different domains of cognitive control. The effect of aging on these functional connectivity patterns was also examined by computing measures of "task differentiation" and "network segregation." This dataset also provides supplemental analyses from the related manuscript (Rieck et al., 2021) to replicate the primary age findings with additional brain atlases. Cognitive neuroscience researchers can benefit from these data by further investigating the age effects on functional connectivity during tasks of cognitive control, in addition to examining the impact of different brain atlases on functional connectivity estimates. These data can also be used for the development of other multi-table and network-based statistical methods in functional neuroimaging.

Ab initio study of the biogenic amino acids.

Ten amino acids have been subjected to the quantum chemical calculations using the ab initio MO-LCAO-SCF calculations. When the geometry optimization started form the X-ray structure confirming the zwitterionic form, the ab initio calculations in vacuo result in the amino acid (canonical) form with the hydrogen atom attached not to the amine but to the carboxylate group. At the optimum geometry, a number of properties were evaluated: dipole moment, dipole polarizability, molecular surface, molecular volume, HOMO, LUMO, ionization energy, and electron affinity using the ΔSCF approach and their values corrected for electron correlation by the 2nd order perturbation theory (MP2). Also, the Mulliken electronegativity and Pearson hardness were evaluated. These properties have been mutually correlated by employing the statistical multivariate methods: the cluster analysis, the probabilistic neural network classifier, the principal component analysis, and the Pearson pair correlation. In addition, the molecular electrostatic potential mapped on the isovalue surface of charge density has been drawn. After the full vibrational analysis, thermodynamic properties at 300 K were evaluated: internal energy, entropy, and the free energy.

Individual Differences in Different Measures of Opioid Self-Administration in Rats Are Accounted for by a Single Latent Variable.

Individual differences in vulnerability to addiction have been widely studied through factor analysis (FA) in humans, a statistical method that identifies "latent" variables (variables that are not measured directly) that reflect the common variance among a larger number of observed measures. Despite its widespread application in behavioral genetics, FA has not been used in preclinical opioid addiction research. The current study used FA to examine the latent factor structure of four measures of i.v. morphine self-administration (MSA) in rats (i.e., acquisition, demand elasticity, morphine/cue- and stress/cue-induced reinstatement). All four MSA measures are generally assumed in the preclinical literature to reflect "addiction vulnerability," and individual differences in multiple measures of abuse liability are best accounted for by a single latent factor in some human studies. A one-factor model was therefore fitted to the data. Two different regularized FAs indicated that a one-factor model fit our data well. Acquisition, elasticity of demand and morphine/cue-induced reinstatement loaded significantly onto a single latent factor while stress/cue-induced reinstatement did not. Consistent with findings from some human studies, our results indicated a common drug "addiction" factor underlying several measures of opioid SA. However, stress/cue-induced reinstatement loaded poorly onto this factor, suggesting that unique mechanisms mediate individual differences in this vs. other MSA measures. Further establishing FA approaches in drug SA and in preclinical neuropsychopathology more broadly will provide more reliable, clinically relevant core factors underlying disease vulnerability in animal models for further genetic analyses.

Reconfiguration and dedifferentiation of functional networks during cognitive control across the adult lifespan.

Healthy aging is accompanied by reduced cognitive control and widespread alterations in the underlying brain networks; but the extent to which large-scale functional networks in older age show reduced specificity across different domains of cognitive control is unclear. Here we use cov-STATIS (a multi-table multivariate technique) to examine similarity of functional connectivity during different domains of cognitive control-inhibition, initiation, shifting, and working memory-across the adult lifespan. We report two major findings: (1) Functional connectivity patterns during initiation, inhibition, and shifting were more similar in older ages, particularly for control and default networks, a pattern consistent with dedifferentiation of the neural correlates associated with cognitive control; and (2) Networks exhibited age-related reconfiguration such that frontal, default, and dorsal attention networks were more integrated whereas sub-networks of somato-motor system were more segregated in older age. Together these findings offer new evidence for dedifferentiation and reconfiguration of functional connectivity underlying different aspects of cognitive control in normal aging.