Variable Mixture Research Articles

Forecasting a Nonstationary Time Series Using a Mixture of Stationary and Nonstationary Factors as Predictors

We develop a method for constructing prediction intervals for a nonstationary variable, such as GDP. The method uses a Factor Augmented Regression (FAR) model. The predictors in the model include a small number of factors generated to extract most of the information in a set of panel data on a large number of macroeconomic variables that are considered to be potential predictors. The novelty of this article is that it provides a method and justification for a mixture of stationary and nonstationary factors as predictors in the FAR model; we refer to this as mixture-FAR method. This method is important because typically such a large set of panel data, for example the FRED-QD, is likely to contain a mixture of stationary and nonstationary variables. In our simulation study, we observed that the proposed mixture-FAR method performed better than its competitor that requires all the predictors to be nonstationary; the MSE of prediction was at least 33% lower for mixture-FAR. Using the data in FRED-QD for the United States, we evaluated the aforementioned methods for forecasting the nonstationary variables, GDP and Industrial Production. We observed that the mixture-FAR method performed better than its competitors.

Hemispheric analysis of mitochondrial Complex I and II activity in the mouse model of ischemia-reperfusion-induced injury

Brain ischemia/reperfusion injury results in a variable mixture of cellular damage, but little is known about possible patterns of mitochondrial dysfunction from the scope of hemispheric processes. The current study used high-resolution fluorespirometry to compare ipsi- and contralateral hemispheres' linked respiration and ROS emission after 60-minutes of filament induced middle cerebral artery occlusion (fMCAo) and 2, 24, 72, and 168 h after reperfusion in mice. Our findings highlight that experimental ischemic stroke resulted in higher mitochondrial respiration in the contralateral compared to the ipsilateral hemisphere and highest ROS emission in ipsilateral hemisphere. The largest difference between the ipsilateral and contralateral hemispheres was observed 2 h after reperfusion in Complex I and II ETS state. Oxygen flux returns to near baseline 72 h after reperfusion without any changes thereafter in Complex I and II respiration. Studying the effects of brain mitochondrial functionality after ischemic stroke in each cerebral hemisphere separately provides a better understanding about the molecular and compensatory processes of the contralateral hemisphere, a region of the brain often neglected in stroke research.

A deconvolution approach to modelling surges in COVID-19 cases and deaths

The COVID-19 pandemic continues to emphasize the importance of epidemiological modelling in guiding timely and systematic responses to public health threats. Nonetheless, the predictive qualities of these models remain limited by their underlying assumptions of the factors and determinants shaping national and regional disease landscapes. Here, we introduce epidemiological feature detection, a novel latent variable mixture modelling approach to extracting and parameterizing distinct and localized features of real-world trends in daily COVID-19 cases and deaths. In this approach, we combine methods of peak deconvolution that are commonly used in spectroscopy with the susceptible-infected-recovered-deceased model of disease transmission. We analyze the second wave of the COVID-19 pandemic in Israel, Canada, and Germany and find that the lag time between reported cases and deaths, which we term case-death latency, is closely correlated with adjusted case fatality rates across these countries. Our findings illustrate the spatiotemporal variability of both these disease metrics within and between different disease landscapes. They also highlight the complex relationship between case-death latency, adjusted case fatality rate, and COVID-19 management across various degrees of decentralized governments and administrative structures, which provides a retrospective framework for responding to future pandemics and disease outbreaks.

A Novel Stochastic Optimizer Solving Optimal Reactive Power Dispatch Problem Considering Renewable Energy Resources

Optimal Reactive Power Dispatch (ORPD is thought of as a noncontinuous, nonlinear global optimization problem. Within the system’s constraints, the ORPD manages to accomplish the reactive power flow. Due to its more intricate linkage of variables, the reactive power issue is more challenging to resolve than the optimum power flow issue. With the existence of renewable energy resources (RERs), solving the ORPD problem to attain the most stable and secure system condition has become a more challenging task. The goal of this article is to solve the objective function of ORPD combined with RERs using a metaheuristic novel optimizer named the African Vultures Optimization Algorithm abbreviated by (AVOA), where the formulation of the ORPD issue including minimization of three single objective functions as follows, voltage deviation, system operating cost, and real power loss, is introduced and also transmission power loss minimization is embraced with the simultaneous incorporation of the optimal renewable energy resources (RERs). Where the ORPD problem complexity grows exponentially with a mixture of continuous and discrete control variables, two distinct continuous and discrete types of optimization variables are considered, and the proposed single objective functions that meet different operating constraints are then transformed into a coefficient multi-objective ORPD problem and elucidated using the weighted sum approach. To validate the suggested algorithm’s effectiveness in addressing the ORPD issue, it is evaluated on three standard IEEE networks: the IEEE-30 bus small-scale network, the IEEE-57 bus medium-scale network, and the IEEE-118 bus large-scale network using different scenarios and the outcomes are compared to these other popular optimization techniques. The findings show that the suggested AVOA algorithm provides an efficient and sturdy high-quality solution for tackling ORPD situations and vastly enhances the overall system performance of power at all scales.

Distance-Based Regression Analysis for Measuring Associations

Distance-based regression model, as a nonparametric multivariate method, has been widely used to detect the association between variations in a distance or dissimilarity matrix for outcomes and predictor variables of interest in genetic association studies, genomic analyses, and many other research areas. Based on it, a pseudo-F statistic which partitions the variation in distance matrices is often constructed to achieve the aim. To the best of our knowledge, the statistical properties of the pseudo-F statistic has not yet been well established in the literature. To fill this gap, the authors study the asymptotic null distribution of the pseudo-F statistic and show that it is asymptotically equivalent to a mixture of chi-squared random variables. Given that the pseudo-F test statistic has unsatisfactory power when the correlations of the response variables are large, the authors propose a square-root F-type test statistic which replaces the similarity matrix with its square root. The asymptotic null distribution of the new test statistic and power of both tests are also investigated. Simulation studies are conducted to validate the asymptotic distributions of the tests and demonstrate that the proposed test has more robust power than the pseudo-F test. Both test statistics are exemplified with a gene expression dataset for a prostate cancer pathway.

Abstract 12: Higher Blood Pressure Trajectories After Endovascular Thrombectomy Are Associated With Cerebral Edema Development

Introduction: High blood pressure after endovascular thrombectomy (EVT) can cause cerebral hyperemia and disrupt the blood-brain barrier. However, its role in cerebral edema development is incompletely understood. In this study, we examined the relationship between post-EVT systolic blood pressure (SBP) trajectories and cerebral edema. Methods: We prospectively enrolled patients with large-vessel occlusion stroke who underwent EVT. Cerebrospinal fluid (CSF) volume was measured using a deep-learning algorithm on CT images at baseline, 24 hours, and 72 hours after stroke. The ratio of CSF volumes between hemispheres was calculated. Automated segmentation of infarct regions on follow-up scans was used to measure net water uptake (NWU), the ratio of density within infarcted tissue relative to the mirrored contralateral region. Latent variable mixture modeling (LVMM) divided patients into SBP trajectory groups during the first 72 hours post-EVT (Fig. 1A). Measures of edema (change in CSF ratio, NWU) were compared between groups. Results: One hundred patients (mean age 70 ± 16, mean NIHSS 15) were analyzed. Edema was assessed by a gradual increase in NWU (20.5, 27.0) at 24 and 72 hours, respectively, and by a reduction in CSF ratio (0.95, 0.78, 0.68) in the affected hemisphere at baseline, 24 hours, and 72 hours, respectively. LVMM identified five SBP trajectories. Higher SBP trajectories were associated with higher NWU (Fig. 1B) but not lower CSF ratio at 24 hours (p<0.001 and p=0.343, respectively). After adjusting for age, admission NIHSS, and TICI score, the moderate-to-high and high-to-moderate trajectory groups were independently associated with higher NWU (aOR 11.40, 95% CI 2.14-20.66) and (aOR 10.97, 95% CI 0.12-21.82), relative to the low and moderate groups. Conclusions: Higher SBP trajectories are associated with an increase in NWU post-EVT. NWU is a promising radiographic biomarker for measuring cerebral edema during the early phase after stroke.

∀ or ∃?

This paper shows that in some axioms regarding the mixture of random variables, the requirement that the conclusions hold for all values of the mixture parameter can be weakened by requiring the existence of only one nontrivial value of the parameter, which need not be fixed. This is the case for the independence, betweenness, and mixture symmetry axioms. Unlike the standard axioms, these weaker versions cannot be refuted by experimental methods.

Interaction Modelling of High Dimensional Production Data

The final goal of this study is to present a practical interaction modeling methodology for big and complex industrial data in which there are mixed difficulty of high dimension, sparsity, interaction, multicollinearity, series bias, low signal noise ratio, nonlinearity, and so on. Particularly, this paper focuses on the issues of high dimension, sparsity, and interactions. In addition, it is necessary to estimate unknown factors and interactions in order to improve overall efficiency in complex and diverse situations of industry. The main purpose here is to summarize the applicability and challenges of Factorization Machines (FMs) to manufacturing data. FMs has possibility because of the ad-vantage of to identify unknown factors and its robustness to data with more than 95% missing values which has already been reported for recommendation problems. Manufacturing data assumed here incudes the inspected quality of the final products, and a set of various variables (settings, sensing results, etc.) of the series production process which may relate to the quality. Common characteristics of manufacturing data include mixture of numerical and categorical variables, high dimensionality, many missing values, and so on. Therefore, this paper particularly focuses on sparse high-dimensional type-mix data. So far, while interaction modeling of high-dimensional data has been discussed and verified, mainly for screening methods, still there is very few studies to apply FMs to the manufacturing data with many numerical variables. There-fore, we report the evaluation results using some synthesis data and simulation data from actual oracle model to summarize FMs applicability as the first step. The second step is to compare the performance of sparse FMs which has feature selection function, beside our proposal.

Perceiving the shape and material properties of 3D surfaces.

Our visual experience of the world relies on the interaction of light with the different substances, surfaces, and objects in our environment. These optical interactions generate images that contain a conflated mixture of different scene variables, which our visual system must somehow disentangle to extract information about the shape and material properties of the world. Such problems have historically been considered to be ill-posed, but recent work suggests that there are complex patterns of covariation in light that co-specify the 3D shape and material properties of surfaces. This work provides new insights into how the visual system acquired the ability to solve problems that have historically been considered intractable.

Diffusion Mixture Minimum Total Error Entropy Adaptive Filtering Algorithm and Its Performance Analysis

The distributed adaptive filtering algorithms applied in the error-in-variables (EIV) model consider that both input and output signals suffer from noise disturbance for practical applications. However, the traditional distributed adaptive filtering algorithms applied in the EIV model cannot combat non-Gaussian noises well. To address this issue, this paper develops a mixture minimum total error entropy (MMTE) method and its diffusion mixture minimum total error entropy (DMMTE) algorithm. The proposed DMMTE has the significant performance improvement in non-Gaussian noises, and is more general than the diffusion minimum total error entropy (DMTE) algorithm. To select mixture coefficient effectively, a variable mixture coefficient method is further proposed to generate a variable DMMTE (VDMMTE) algorithm. Moreover, the local mean stability analysis and steady-state performance analysis of DMMTE are presented. Simulations verify the correctness of the theoretical analysis and illustrate the performance superiority of the proposed DMMTE.

Mapping the Insomnia Severity Index Instrument to EQ-5D Health State Utilities: A United Kingdom Perspective.

This study aimed to map the Insomnia Severity Index (ISI) to the EQ-5D-3L utility values from a UK perspective. Source data were derived from the 2020 National Health and Wellness Survey (NHWS) for France, Germany, Italy, Spain, the UK and the US. Ordinary least squares regression, generalised linear model (GLM), censored least absolute deviation, and adjusted limited dependent variable mixture model (ALDVMM) were employed to explore the relationship between ISI total summary score and EQ-5D utility while accounting for adjustment covariates derived from the NHWS. Fitting performance was assessed using standard metrics, including mean-squared error (MSE) and coefficient of determination (R2). A total of 17,955 respondent observations were included, with a mean ISI score of 12.12±5.32 and a mean EQ-5D-3L utility (UK tariff) of 0.71±0.23. GLM gamma-log and ALDVMM were the two best performing models. The ALDVMM had better fitting performance (R2=0.320, MSE 0.0347) than the GLM gamma-log (R2=0.303, MSE 0.0353); in train-test split-sample validation, ALDVMM also slightly outperformed the GLM gamma-log model, with an MSE of 0.0351 versus 0.0355. Based on fitting performance, ALDVMM and GLM gamma-log were the preferred models. In the absence of preference-based measures, this study provides an updated mapping algorithm for estimating EQ-5D-3L utilities from the ISI summary total score. This new mapping not only draws its strengths from the use of a large international dataset but also the incorporation of adjustment variables (including sociodemographic and general health characteristics) to reduce the effects of confounders.

Catalog of EQ-5D-3L Health-Related Quality-of-Life Scores for 199 Chronic Conditions and Health Risks in Denmark.

A Danish national representative catalog of health-related quality-of-life scores for 199 chronic conditions is presented, which provides population estimates for chronic conditions subgroups that can be used for health economic evaluation.Two separate regression models of EQ-5D-3L utility scores with different sets of control variables are estimated to allow researchers to adjust for differences in the composition of the subgroups and provide a tool that can be used in other settings.Results indicate that health-related quality of life varies across disease groups but is lowest for renal disease, mental and behavioral disorders, benign neoplasms and diseases of the blood, digestive systems, and nervous systems.Health risks and lifestyle factors such as perceived stress, loneliness, and a large body mass index are highly correlated with health-related quality of life, and, in many cases, the correlation is higher than with individual diseases.

Applications of Six Sigma and Quality by Design Approach in UHPLC Method Development for Simultaneous Assessment of Eight Bronchodilators in Drug Substance

This work portrays the development and validation of a simple and fast UHPLC method for the simultaneous estimation of eight bronchodilators in the drug substance. Process and performance capability (Cpk, Ppk, PPM) studies were conducted by which it may easily conclude how well developed method meeting the requirements. Integration of quality by design (QbD) concept to analytical method development was also discussed. The temperature and mobile phase interaction on chromatographic separation were studied using design of experiments (DoE). Using the QbD and design of experiments (DoE) as combination, a design space was established for developed method. The developed method facilitates the quantitative determination of clenbuterol hydrochloride, fluticasone, formoterol, glycopyrrolate, levalbuterol, metaproterenol, salmeterol and theophylline as a drug substance. All the compounds were separated within 20 min using analytical column X-Bridge BEH C18; (100 × 2.1 mm, 2.5 μ) mobile phase consisted variable mixtures of mobile phase A (10 mM ammonium acetate pH 4.5) and mobile phase B (methanol) with the gradient elution, column temperature 30 ºC, mobile phase flow rate 0.2 mL min/min, wavelength of detection 234 nm.

Adrenal Myelolipoma. An Interesting Case Report

Introduction: Adrenal myelolipoma is a rare urological lesion, benign in nature, and composed of a variable mixture of mature adipose and hematopoietic elements. The true incidence of these tumours is not well known. Despite their benign biology, these lesions can be a cause of dilemma for a urologist; we describe a case of incidental diagnosis of adrenal myelolipoma in a patient who presented with upper abdominal pain and reviews the literature on its aetiology, diagnosis, and management. Finally, the optimal treatment for myelolipoma depends on the size and symptoms of the mass and the needs of the patient. Case Report: Here we present a case of a 60-year-old female who presented with chronic left-sided flank pain and right upper abdomen that had been worsening over the past three weeks. Conclusion: Myelolipoma is a rare, non-functional, benign tumour. Patients having high blood pressure don't respond well to antihypertensive. They respond effectively to the medication after the removal of the tumour. So we recommend that all patients with myelolipoma not responding well to non-surgical management should plan for surgery for a better outcome.

Linearized Boltzmann Collision Operator: I. Polyatomic Molecules Modeled by a Discrete Internal Energy Variable and Multicomponent Mixtures

The linearized Boltzmann collision operator appears in many important applications of the Boltzmann equation. Therefore, knowing its main properties is of great interest. This work extends some classical results for the linearized Boltzmann collision operator for monatomic single species to the case of polyatomic single species, while also reviewing corresponding results for multicomponent mixtures of monatomic species. The polyatomicity is modeled by a discrete internal energy variable, that can take a finite number of (given) different values. Results concerning the linearized Boltzmann collision operator being a nonnegative symmetric operator with a finite-dimensional kernel are reviewed.A compactness result, saying that the linearized operator can be decomposed into a sum of a positive multiplication operator, the collision frequency, and a compact operator, bringing e.g., self-adjointness, is extended from the classical result for monatomic single species, under reasonable assumptions on the collision kernel. With a probabilistic formulation of the collision operator as a starting point, the compactness property is shown by a splitting, such that the terms can be shown to be, or be the uniform limit of, Hilbert-Schmidt integral operators and as such being compact operators. Moreover, bounds on - including coercivity of - the collision frequency are obtained for a hard sphere like model, from which Fredholmness of the linearized collision operator follows, as well as its domain.

A next-generation approach to mental health outcomes: Treatment, time, and trajectories

Over the last several decades, inpatient psychiatric length of stay (LOS) has been greatly reduced to the detriment of patients. Latent variable mixture modeling, can be used to improve the quality of care for patients by identifying unobserved subgroups and optimize treatment variables, including LOS. This study had three objectives (1) to replicate the findings made by Oh et al. in a distinct sample, (2) to examine demographic differences related to inpatient treatment trajectories, and (3) to relate additional variables to each trajectory. We collected data on six key mental illness factors and information on felonies, misdemeanors, history of stopping psychiatric medication and psychotherapy, length of time in psychotherapy, and the number of therapists and psychiatrists from 489 patients at an inpatient psychiatric hospital. We derived latent mental illness scores after applying growth mixture modeling to these data. We identified three distinct trajectories of mental illness change: High-Risk, Rapid Improvement (HR-RI), Low-Risk, Partial Response (LR-PR), and High-Risk, Gradual Improvement (HR-GI). The HR-GI group was more likely to have patients who were female, Asian, younger, Yearly Income (YI) <$20,000, that spent more time in psychotherapy throughout their life, and had the longest LOS while inpatient. The LR-PR group had was more likely to be male, Hispanic/Latino and multiracial, older, YI >$500,000, have a history of misdemeanors, and this group had the shortest LOS (p < .05). These findings replicate and extend our previous findings in Oh et al. (2020a) and highlight the clinical utility of agnostically determining the treatment trajectories.

Sex-specific Trajectories of Insulin Resistance Markers and Reduced Renal Function During 18 Years of Follow-up: TLGS.

The evidence suggest that insulin resistance (IR) complicates chronic kidney disease (CKD); however, the longitudinal association of IR with development of CKD is unknown. This work aimed to investigate the association between the dynamic course of insulin resistance and CKD. In the longitudinal, population-based Tehran Lipid and Glucose Study, 3071 eligible participants aged 20 years or older were followed for 18 years at 3-year intervals. Homeostatic model assessment of insulin resistance (HOMA-IR) and clinical surrogate markers of IR, including triglyceride-glucose index (TyG), visceral adiposity index (VAI), and lipid accumulation product (LAP), were calculated. Using latent variable mixture modeling, sex-specific trajectories were plotted for each IR marker. Trajectory group association of the IR markers with CKD was determined using the multivariable Cox proportional-hazards regression model. For HOMA-IR, 2 distinct trajectory patterns (stable and increasing), and for TyG, VAI, and LAP, 3 trajectories (low, moderate, and high) were identified. The participants with an increasing HOMA-IR trajectory had a significantly increased risk of CKD in men (hazard ratio [HR]: 1.72; 95% CI, 1.06-2.79) and women (HR: 1.37; 95% CI, 1.00-1.89) after adjusting for confounding variables. The high TyG and VAI trajectory classes were associated with a higher risk of CKD than the low TyG and VAI trajectory classes both in men (TyG: HR: 1.97; 95% CI, 1.12-3.46; VAI: HR:1.66; 95% CI, 1.06-2.62) and women (TyG: HR: 1.50; 95% CI, 1.06-2.12; VAI: HR:1.66; 95% CI, 1.20-2.31). In contrast, the high LAP (HR: 3.38; 95% CI, 2.08-5.48) trajectory was associated with incident CKD only in women. An increasing trend of HOMA-IR is associated with a higher risk of CKD in men and women. Among clinical IR surrogate markers, abnormal trajectory patterns of LAP in women and TyG and VAI in both sexes are associated with a higher risk of CKD.

SIGNAL DETECTION AND VALIDATION IN AN ERA OF BIG GERONTOLOGICAL DATA

Abstract Older adult health assessment long has posed measurement challenges—multidimensionality of sentinel outcomes like functioning and frailty, for example. This presentation discusses three developments creating opportunities for gerontologic biostatistics (GBS) over the past 10 years. Firstly, modeling to internally validate measurements or to quantify systematic heterogeneity in assessing older adult health has become considerably more widespread. Confirmatory latent variable modeling, harmonization, and mixture models will be addressed. Secondly, signal intensive behavioral phenotypes are proliferating, e.g. accelerometry, sleep actigraphy, and ecological momentary assessment. Functional data analysis is described as a data analytic technique to extract signal capturing main behavioral features or most relevant for health outcomes. Thirdly, “deep” characterization is under hot pursuit—whether by single- or multi-‘omics studies, or by multi-modal phenotyping as is increasingly common in the study of cognition. Techniques to accomplish this replicably are discussed. Throughout, potential pitfalls and implications for gerontologic data science development are identified.

Lung nodule detection algorithm based on rank correlation causal structure learning

Early diagnosis can significantly improve the survival rate of lung cancer patients. This study attempts to construct a causal structure network between the computational and semantic features of lung nodules through causal discovery algorithms, and to detect and prevent lung nodules based on this network. For complex and diverse lung nodule data sets, this paper proposes a new causal lung nodule detection algorithm Tau-CSFS in combination with rank correlation methods. The algorithm can effectively mine the causal relationship among lung cancer data that obey the non-linear non-Gaussian distribution, and the mixture of continuous and discrete variables, and has good predictive performance. We made three main contributions. First, we proved that the Kendall rank correlation coefficient that does not require data distribution can be used as a standard for independence test. Second, we applied Kendall rank correlation to Bayesian structure learning, and proposed a new causal discovery algorithm: Tau-CS algorithm based on hypothesis testing. The third contribution is to combine the Tau-CS algorithm with the feature selection method, and further propose the Tau-CSFS algorithm, which solves the problem of causality mining and diagnosis detection of lung nodule data. In the experiment, the Tau-CS algorithm is compared with the prior art on 7 Bayesian networks on the additive noise structure model, and it is proved that the algorithm has a better accuracy of causal structure learning. Finally, in the lung nodule detection stage, using the processed LIDC data set to perform two-classification and multi-classification experiments on seven semantic categories, the average accuracy of the Tau-CSFS algorithm reached 85.84% and 83.32%. The Tau-CSFS algorithm are better than comparable similar algorithms in the comprehensive performance index. The results show that the proposed algorithm has good detection performance and wide application prospects.

The Andaluca plutonic unit, Vinquis Intrusive Complex, Argentina: An assessment of mantle role in the genesis of Early Carboniferous weakly peraluminous A-type granites in the pre-Andean SW Gondwana margin

During the Early Carboniferous, significant intracontinental magmatism was developed in the retro-arc region of the SW margin of Gondwana between ca. 27° and 31°S. This magmatism consisted of metaluminous to weakly peraluminous A-type granites, strongly peraluminous A-type granites (A- to S- hybrid granitoids) and, in a lesser extent, (sub-)volcanic felsic (rhyolites and trachytes) and mafic (alkaline basalts and lamprophyres) rocks. The Vinquis Intrusive Complex (VIC) registers these two compositional varieties of A-type granites: the Vinquis Unit (VU) represents the strongly peraluminous granites and the Andaluca Unit (AU) the weakly peraluminous granites. The adjacent Zapata Intrusive Complex (ZIC) also contains metaluminous to weakly peraluminous A-type granites. The AU is a semi-elliptical plutonic body of approximately 60 km 2 located in the southwestern part of the VIC, intruding the Vinquis Unit. The AU comprises three dominant rock types: i) quartz monzonite, ii) syenogranite and iii) alkali feldspar granite that can be classified as F-rich (calculated F content: 1950–5700 ppm), ferroan (FeO t /(FeO t + MgO) = 0.86–0.95) and weakly peraluminous (ASI = 1.03–1.10) A-type (Zr + Nb + Ce + Y = 364–570 ppm; Ga = 20–23 ppm) granitoids (SiO 2 wt% = 67.8–75.4% wt%). This unit was emplaced in early Carboniferous time (U Pb zircon crystallization ages of 346 ± 3 Ma and 342 ± 3 Ma). The AU has 87 Sr/ 86 Sr t , εNd t and εHf t values ranging from 0.7092 to 0.7140, −2.6 to −3.6 and − 16.5 to −3.3, respectively. These isotopic suggest that the parental magma of AU dominantly involved variable mixtures of mantle-derived components (magmas/fluids) and peraluminous continental crust. The extremely felsic granites of the AU (SiO 2 > ~72%) with a strongly fractionated composition (Rb > 336 and Sr < 70 ppm and Eu/Eu* < 0.22) supports extensive fractional crystallization. The high contents of fluorine and HFSE in AU could be explained as derived from an F- HFSE- and alkali-rich parental mafic magma plus contributions from F-bearing minerals assimilated from the country rock. Likewise, the magmatic differentiation processes from these reduced mafic magmas could explain the ferroan character of the AU granitoids. We envision three stages in the origin of these rocks: (1) fluids and magmas from the asthenospheric mantle metasomatize and melt the subcontinental lithospheric mantle (SCLM), the alkali-rich mafic melts produced are emplaced at the base of the crust, transfer heat to the crust and melt it, generating strongly peraluminous A-type magmas; (2) continued introduction of alkali-rich mafic magmas into the deep crust produce extensive assimilation of the preheated crust in equilibrium with mafic cumulates (generated by reaction and/or fractionation); (3) the hot magmas so produced migrate into the middle-upper crust to produce ferroan weakly peraluminous A-type granitoids. • The Andaluca plutonic unit of the Vinquis Intrusive Complex crystallized between 346 ± 3 Ma and 342 ± 3 Ma. • Andaluca Unit shows a F-rich weakly peraluminous ferroan A 2 -type affinity. • The high contents of fluorine and HFSE in Andaluca Unit can be explained from an F-rich parental alkaline mafic magma. • The Andaluca Unit granitoids can be generated through combined crustal assimilation and fractional crystallization of magmas derived from the metasomatized subcontinental lithospheric mantle.