Discrepancy Function Research Articles

Decentralized control law for load-adaptive gaits of multi-legged robots inspired by millipedes

ABSTRACT Millipedes increase the duty ratio and wavelength of their gait to enhance thrust force against resistive load. However, the underlying control mechanisms responsible for generating such adaptive gaits remain elusive. In this study, we proposed a decentralized control law based on the concept of ‘discrepancy function’. Our proposed walking controller successfully reproduced the load-adaptive gaits of millipedes on the inclined ground. Furthermore, we evaluated the adaptability of our controller to horizontal resistive forces that hinder forward walking. Our simulation results suggest that the millipede-inspired controller could be beneficial for multi-legged robots equipped with leg actuators operating under limited motor outputs.

Genome-wide analysis of UDP-glycosyltransferase family in Citrus sinensis and characterization of a UGT gene encoding flavonoid 1–2 rhamnosyltransferase

UDP-glycosyltransferases (UGTs) play a crucial role in the glycosylation of secondary metabolites in plants, which is of significant importance for growth and response to biotic or abiotic stress. Despite the wide identification of UGT family members in various species, limited information is available regarding this family in citrus. In this study, we identified 87 UGT genes from the Citrus sinensis genome and classified them into 14 groups. We characterized their gene structures and motif compositions, providing insights into the molecular basis underlying discrepant functions of UGT genes within each evolutionary branch. Tandem duplication events were found to be the main driving force behind UGT gene expansion. Additionally, we identified numerous cis-acting elements in the promoter region of UGT genes, including those responsive to light, growth factors, phytohormones, and stress conditions. Notably, light-responsive elements were found with a frequency of 100 %. We elucidated the expression pattern of UGTs during fruit development in Citrus aurantium using RNA-seq and quantitative real-time PCR (qRT-PCR), revealing that 10 key UGT genes are closely associated with biosynthesis of bitter flavanone neohesperidosides (FNHs). Furthermore, we identified Ca1,2RhaT as a flavonoid 1–2 rhamnosyltransferase (1,2RhaT) involved in FNHs biosynthesis for the first time. Isolation and functional characterization of the gene Ca1,2RhaT from Citrus aurantium in vitro and in vivo indicated that Ca1,2RhaT encoded a citrus 1,2RhaT and possessed rhamnosyl transfer activities. This work provides comprehensive information on the UGT family while offering new insights into understanding molecular mechanisms regulating specific accumulation patterns of FNHs or non-bitter flavanone rutinosides (FRTs) in citrus.

Actor-Critic With Synthesis Loss for Solving Approximation Biases.

Approximation biases of value functions are considered a key problem in reinforcement learning (RL). In particular, existing RL algorithms are hindered by overestimation and underestimation biases, i.e., value mismatching between RL's actual returns and action-value approximations limits the performance of RL algorithms. In this article, we first develop a new synthesis loss function for RL's action-value estimation integrating a regularization term and a modified "clipped double Q-learning" structure for solving overestimation and underestimation biases. To minimize the differences between action-value estimations and actual returns in RL, we develop a new discrepancy function to determine the type and magnitude of approximation biases. Then, two coefficients embedded in the synthesis loss are automatically tuned by minimizing the discrepancy function during training to minimize approximation biases. We further design a new actor-critic (AC) algorithm, named AC with synthesis loss (ACSL), by integrating the synthesis loss function and an error-controlled mechanism. Experimental results on continuous control tasks illustrate that the proposed ACSL algorithm outperforms other cutting-edge RL methods in many tasks and that the proposed synthesis loss function is easily implemented into other algorithms and significantly reduces approximation biases while improving performance. The proposed method can successfully handle many complex continuous control tasks and can greatly outperform other state-of-the-art algorithms on most tasks.

Bayesian calibration and uncertainty quantification of a rate-dependent cohesive zone model for polymer interfaces

This study presents a rate-dependent cohesive zone model for the fracture of polymeric interfaces and performs a Bayesian calibration, an uncertainty quantification, and a sensitivity analysis for the model. The proposed cohesive zone model accounts for both reversible elastic and irreversible rate-dependent separation sliding deformation at the interface. The viscous dissipation due to the irreversible opening at the interface is modeled using elastic-viscoplastic kinematics that incorporates the effects of strain rate. Inverse calibration of parameters for such complex models through trial and error is challenging due to the large number of parameters of the model. Moreover, the calibrated parameter values are often non-unique and uncertain when the available experimental data is limited. To tackle this challenge, we employ a Bayesian calibration approach to identify parameters from experimental data, the resulting parameters significantly enhance the accuracy of the model. To quantify the uncertainty associated with the inverse parameter estimation, a modular Bayesian approach is employed to calibrate the unknown model parameters, accounting for the parameter uncertainty of the cohesive zone model. The advantages of the Bayesian calibration over a deterministic parameter fit are demonstrated. Further, to quantify the model uncertainties, such as incorrect assumptions or missing physics, a discrepancy function is introduced, which significantly improves the model’s prediction. Finally, the total uncertainty of the model is quantified in a predictive setting. A sensitivity analysis is performed to assess how changes in the input variables of the model affect the peak load, facilitating the identification of a concise set of highly influential parameters. The present approach can be used for calibration and uncertainty quantification for other complex computational mechanics models. It should also facilitate the designing of interface materials under uncertainty.

Sleep discrepancy and cognitive function in community-dwelling older adults.

This was the first study to use cluster analysis to characterise sleep discrepancy (the discordance between self-reported and objective sleep) across multiple sleep parameters, in community-dwelling older adults. For sleep efficiency, negative discrepancy (the tendency to self-report worse sleep than objectively-measured) was associated with poorer memory, independent of insomnia severity, depressive symptoms and objective sleep. This suggests a unique role for sleep discrepancy as a possible risk factor for future cognitive decline, and warrants the need for further research.

Subjective Cognitive Decline in Community-Dwelling Older Adults With Objectively Normal Cognition: Mediation by Depression and Instrumental Activities of Daily Living.

Subjective cognitive decline (SCD) refers to self-reported memory loss despite normal cognitive function and is considered a preclinical stage of Alzheimer's disease. This study aimed to examine the mediating effects of depression and Instrumental Activities of Daily Living (IADL) on the association between the scoring of Clinical Dementia Rating Scale-Sum of Boxes (CDR-SB) and Subjective Cognitive Decline Questionnaire (SCD-Q). A sample of 139 community-dwelling older adults aged 65-79 with normal cognitive function completed the SCD-Q, a comprehensive neuropsychological battery, and functional/psychiatric scales. We conducted 1) a correlation analysis between SCD-Q scores and other variables and 2) a path analysis to examine the mediating effects of depression and IADL on the relationship between CDR-SB and SCD-Q. CDR-SB was found to be indirectly associated with SCD-Q, with depressive symptoms mediating this relationship. However, no direct association was observed between SCD-Q and CDR-SB. Additionally, IADL was not associated with SCD-Q and did not mediate the relationship between CDR-SB and SCD-Q. The model fit was acceptable (minimum discrepancy function by degrees of freedom divided [CMIN/DF]=1.585, root mean square error of approximation [RMSEA]=0.065, comparative fit index [CFI]=0.955, Tucker-Lewis index [TLI]=0.939). Our results suggest that SCD-Q is influenced by depressive symptoms, but not by IADL. The role of depressive symptoms as a mediator between CDR-SB and SCD-Q indicates that psychological factors may contribute to the perception of SCD. Therefore, interventions targeting depression may mitigate the concerns associated with SCD and reduce feelings of worse performance compared to others of the same age group.

Attachment and negative affect on mental health and pain experience patients with Multiple Sclerosis: Mediated by coping strategies and loneliness

BackgroundMultiple sclerosis and its related stressors significantly affect the mental health of people with MS. Considering the progressive and relapsing-remitting nature of the disease and somatic complaint, the Pain experience for MS patients is challenging. We examined the direct effects of Attachment and the negative affect on mental health and pain experience and the indirect effects of Attachment, negative affect, loneliness, and coping strategies on the mental health and pain experience of people with multiple sclerosis. MethodsThree hundred forty-five patients with MS were selected through the Iranian MS Association. Measures included the negative affect (PANAS), Adult Attachment Inventory (AAI), General Health Questionnaire (GHQ28), short-form McGill pain questionnaire (SF-mpq-2), Social and emotional loneliness scale for adults (SELSA-S), Coping Inventory for Stressful Situations(CISS-21). The present study has employed the Structural Equation Model (SEM) to investigate the direct and indirect effects of coping strategies, attachment, loneliness, and negative affect on mental health and pain experienced by individuals with MS. The fit of the model to the data was examined using the Discrepancy Function Divided by Degrees of Freedom (CMIN/DF), Normed Fit Index (NFI), TuckerLewis Index (TLI), Comparative Fit Index (CFI), and Root Mean Square Error of Approximation (RMSEA). ResultsThe fit indices results showed that the model's fit was good. Furthermore, findings indicate that 13 % (R2=13) of the pain experience Changes and 47 % (R2=47) of the mental health Changes are explained via study predictors. Negative affect directly affects mental health and pain experience, and Attachment directly affects mental health. Negative affect indirectly affects coping strategies. Attachment and Negative affect indirectly affect loneliness. Loneliness and coping strategies indirectly affect mental health and pain experience. ConclusionStudy findings contribute to our understanding of the crucial structures that play a role in the mental health and pain experience of individuals with MS. Loneliness and coping strategies as mediating variables play essential roles in these people's mental health and pain experience. In the times ahead, it would be beneficial to prioritize addressing negative affect, attachment, coping strategies, and loneliness in the patients with MS' medical and psychological intervention.

Pituitary Hyperplasia and Overt Hypothyroidism Induced by Methimazole in an Adolescent Girl with Resistance to Thyroid Hormone Accompanying Hashimoto’s Thyroiditis: A Case Report

We describe a 13-year-old girl who presented at her local hospital with a diffuse goiter and had discrepant thyroid function test (TFT) of elevated free T4 (FT4), free T3 (FT3) levels with mildly elevated thyroid-stimulating hormone (TSH) and a pituitary magnetic resonance imaging (MRI) report of a pituitary hyperplasia. She was referred to our hospital where a repeat TFT found low FT4 and high TSH levels, and high levels of antithyroid peroxidase (anti-TPO) and anti-thyroglobulin (anti-TG) antibodies, leading to the diagnosis of Hashimoto’s thyroiditis (HT) with overt primary hypothyroidism. The girl had a good response after daily 100 µg levothyroxine treatment for 8 months with decreased goiter size along with disappearance of the pituitary mass. However, her FT4 and FT3 levels were elevated while the TSH was in the high normal range, although at this time there were no signs of hyperthyroidism. A genetic study confirmed our provisional diagnosis that the patient had a p.Pro453Thr monoallelic loss-of-function mutation of the thyroid hormone receptor beta (THRB) gene, suggesting the diagnosis of coexisting resistance to thyroid hormone-β (RTHβ) and HT in this patient.

Challenging diagnosis of resistance to thyroid hormone in a patient with COVID-19, pituitary microadenoma and unusual response to octreotide long-acting release test.

The resistance to thyroid hormone syndrome (RTHβ) occurs uncommonly and requires a high level of clinical suspicion and specific investigations to reach a precise diagnosis and to avoid unnecessary and potentially harmful therapies. We report a case of a young male patient referred to our unit for SARS-CoV-2 infection and atrial fibrillation with elevated thyroid hormones and non-suppressed thyroid-stimulating hormone (TSH), for which antithyroid therapy was prescribed. A mood disorder was reported in the medical history. The family history was unknown as the patient was adopted. Thyroid-specific antibodies were undetectable, and thyroid ultrasound revealed a normal thyroid gland without nodules. After the resolution of SARS-CoV-2 infection, the diagnostic workup continued, and the pituitary MRI revealed a small area ascribable to a microadenoma. Due to atrial fibrillation, the execution of the T3 test was contraindicated. The octreotide long-acting release (LAR) test showed an initial reduction of free thyroid hormones levels at first administration, which was consistent with the presence of a TSH-secreting pituitary tumour, although an escape from the response was observed after the following two injections of octreotide LAR. Indeed, the genetic investigation revealed a variant in heterozygosity of the THRβ gene (Pro453Ser), thus leading to an RTHβ diagnosis, and, therefore, medical treatment with triiodothyroacetic acid was initiated. After 2 years from the SARS-CoV-2 infection, the patient continues the follow-up at our outpatient clinic, and no other medical interventions are needed. RTHβ is a rare genetic syndrome characterised by discrepant thyroid function tests and by a dissociation between the observed hormone levels and the expected patient signs and symptoms. Features of thyroid hormone deficiency in TR-ß dependent tissues (pituitary gland, hypothalamus, liver and neurosensitive epithelia), as well as thyroid hormone excess in TR-α-dependent tissues (heart, bone, skeletal muscle and brain), may coexist in the same individual. Clinical pictures can be different even when the same variant occurs, suggesting that other genetic and/or epigenetic factors may play a role in determining the patient's phenotype. Differentiating RTHβ from a TSH-secreting pituitary tumour is very difficult, especially when a concomitant pituitary adenoma is detected during diagnostic workup. The injection of long-acting somatostatin analogues can help differentiate the two conditions, but it is important to detect any interference in the dosage of thyroid hormones to avoid an incorrect diagnosis. Genetic testing is fundamental to prevent unnecessary and potentially harmful therapies. Medical treatment with triiodothyroacetic acid was demonstrated to be effective in reducing thyroid hormone excess and controlling symptoms.

Safe and Scalable Real-Time Trajectory Planning Framework for Urban Air Mobility

This paper presents a real-time trajectory planning framework for urban air mobility (UAM) that is both safe and scalable. The proposed framework employs a decentralized, free-flight concept of operation in which each aircraft independently performs separation assurance and conflict resolution, generating safe trajectories by accounting for the future states of nearby aircraft. The framework consists of two main components: a data-driven reachability analysis tool and an efficient Markov-decision-process-based decision maker. The reachability analysis overapproximates the reachable set of each aircraft through a discrepancy function learned online from simulated trajectories. The decision maker, on the other hand, uses a 6-degree-of-freedom guidance model of fixed-wing aircraft to ensure collision-free trajectory planning. Additionally, the proposed framework incorporates reward shaping and action shielding techniques to enhance safety performance. The proposed framework was evaluated through simulation experiments involving up to 32 aircraft in a generic city-scale area with a 15 km radius, with performance measured by the number of near-midair collisions (NMAC) and computational time. The results demonstrate the planner’s ability to generate safe trajectories for the aircraft in polynomial time, showing its scalability. Moreover, the action shielding and reward shaping strategies show up to a 78.71 and 85.14% reduction in NMAC compared to the baseline planner, respectively.

Efficient calibration of computer models with multivariate output

The classical calibration procedures of computer models only concern the univariate output, which would not be satisfied in practice. Multivariate output is gradually more prevalent in a wide range of real-world applications, which motivates us to develop a new calibration procedure to extend the classical calibration methods to multivariate cases. In this work, we propose an efficient calibration procedure for multivariate output within restricted correlation. First, we construct an estimator of the discrepancy function between the true process and the computer model by the local linear approximation, then obtain an estimator of the calibration parameter by the weighted profile least squares and establish its asymptotic properties. In addition, we also develop an estimator of the calibration parameter in a special situation, whose asymptotic normality has been derived. Numerical studies including simulations and an application to composite fuselage simulation verify the efficiency of the proposed calibration procedure.

Error approximation and bias correction in dynamic problems using a recurrent neural network/finite element hybrid model

This work proposes a hybrid modeling framework based on recurrent neural networks (RNNs) and the finite element (FE) method to approximate model discrepancies in time-dependent, multi-fidelity problems, and use the trained hybrid models to perform bias correction of the low-fidelity models. The hybrid model uses FE basis functions as a spatial basis and RNNs for the approximation of the time dependencies of the FE basis' degrees of freedom. The training data sets consist of sparse, non-uniformly sampled snapshots of the discrepancy function, pre-computed from trajectory data of low- and high-fidelity dynamic FE models. To account for data sparsity and prevent overfitting, data upsampling and local weighting factors are employed, to instigate a trade-off between physically conforming model behavior and neural network regression. The proposed hybrid modeling methodology is showcased in three highly non-trivial engineering test-cases, all featuring transient FE models, namely, heat diffusion out of a heat sink, eddy-currents in a quadrupole magnet, and sound wave propagation in a cavity. The results show that the proposed hybrid model is capable of approximating model discrepancies to a high degree of accuracy and accordingly correct low-fidelity models.

Screening the Discrepancy Function of a Computer Model

Traditionally, screening refers to the problem of detecting influential (active) inputs in the computer model. We develop methodology that applies to screening, but the main focus is on detecting active inputs not in the computer model itself but rather on the discrepancy function that is introduced to account for model inadequacy when linking the computer model with field observations. We contend this is an important problem as it informs the modeler which are the inputs that are potentially being mishandled in the model, but also along which directions it may be less recommendable to use the model for prediction. The methodology is Bayesian and is inspired by the continuous spike-and-slab prior popularized by the literature on Bayesian variable selection. In our approach, and in contrast with previous proposals, a single MCMC sample from the full model allows us to compute the posterior probabilities of all the competing models, resulting in a methodology that is computationally very fast. The approach hinges on the ability to obtain posterior inclusion probabilities of the inputs, which are easy to interpret quantities, as the basis for selecting active inputs. For that reason, we name the methodology PIPS—posterior inclusion probability screening.

Adaptation and validation of the west haven-yale multidimensional pain inventory for Pakistani population.

To evaluate the adapted version of West Haven-Yale Multidimensional Pain Inventory for patients with chronic pain. The multiphase study was conducted from January to December 2021. The factorial structure of the Urdu version of West Haven-Yale Multidimensional Pain Inventory was evaluated on a sample of adult patients aged 18- 45 years with non-specific chronic pain, taken from public and private hospitals and clinics of Lahore, Pakistan. The Urdu version was then subjected to factor analysis, while Cronbach's alpha, composite reliability, convergent and discriminant validity of the scale were also calculated. Data was analysed using SPSS 24. Of the 306 subjects, 204(66.7%) were females and 102(33.3%) were men. The overall mean age was 30.94+/-8.44 years. There were 166(54.2%) subjects who were married, and 137(44.8%) reported experiencing pain daily. The confirmatory factor analysis showed a 45-item structure for 12 sub-scales as the best fit. The statistics for the final model were observed as minimum discrepancy function by degrees of freedom divided was 1.69, root mean square error of approximation was 0.05, and standardised root mean square residual was 0.06. Comparative fit index value was 0.91 and Tucker-Lewis coefficient was 0.90. Cronbach's alpha reliability ranged between 0.68 and 0.89 for the subscales, while for the total scale, it was 0.72. The Urdu version of West Haven-Yale Multidimensional Pain Inventory was found to be a reliable and valid tool for chronic pain assessment for patients in Pakistan.

New insights from paediatric electrophysiology

Visual electrophysiology is uniquely valuable in paediatric practice because it provides objective measures of visual pathway function. The combination of electroretinograms and visual evoked potentials to flashes and patterns enables a functional assessment of the entire pathway, from photoreceptors to striate cortex. This information can be impossible to obtain from children by other means. Electrophysiology complements structural information, often highlighting discrepant structure function relationships.Objective functional electrophysiology facilitates early diagnosis and can delineate potential treatment windows from the youngest ages. Thus visual electrophysiology findings in children contribute to natural history and deep phenotyping studies that inform clinical trials of new treatments not only for ocular disease, but also systemic diseases that involve the eye.This talk will describe ways in which ISCEV tests can be adapted and optimized for young children in clinical practice without loss of diagnostic accuracy, and how electrophysiology can distinguish phenotypes and inform genetic analysis. Examples of the value and limitations of electrophysiology in potential gene trials and other ameliorating treatments will be presented and discussed; including inherited retinal disease such as RPE65, neurodegenerations that affecting the eye such as CLN2 and iron brain disorders such as PKAN and ciliopathies such as BBS.Key words: visual electrophysiology, children, ERG, VEP, ISCEV.

Conirmatory Factor Analysis of Depression, Anxiety and Stress 21 (DASS 21) scale in a study on Occupational Health Psychology in Health Care Professionals from Anand, Gujarat

Introduction: Confirmatory factor analysis (CFA) is a statistical technique to verify the factor structure of a set of observed variables. Objective: The study was conducted with an objective to study the psychometric properties of DASS 21 scale by Confirmatory factor analysis Method: This study used a Multi Method Design (Multiple approach Design), one was a cross sectional study of 228 participants and the other was a Solomon 4 group Design with total 64 participants. Four groups of health care professionals viz Allopathic doctors, Physiotherapists, Nursing professionals and Community Health Officers (CHOs) were chosen for the study. Convenience sampling was done at two stages. Results: Confirmatory factor analysis was used to evaluate the construct validity of DASS 21 scale. The model was overidentified as the estimated parameters were less than the number of observed variances and covariances. CMIN/DF(Chi square (minimum discrepancy function) /Degree of freedom) value was 2.021 which was < 5. Goodness of Fit Index (GFI) value (0.892) was near to .9. Root Mean square Residuals (RMR) and Root Mean Square Error of Approximation (RMSEA) value were 0.031 and 0.063 respectively which were less than 0.08. Average variance extracted (AVE) for the three constructs were less than the squared interconstruct correlation. Conclusion: DASS-21 scale demonstrated a good convergent validity, but in this study, discriminant validity was found to be poor as determined by average variance extracted in comparison to squared Interconstruct correlation.

Structure identification for a linearly structured covariance matrix: part II

Summary Covariance matrices with a linear structure are widely used in multivariate analysis. The choice of covariance structure can be made from a set of possible linear structures. As a result, the most appropriate structure is determined by minimizing the discrepancy function. This paper is a continuation of previous work on identifying linear structures with an entropy loss function as a discrepancy function. We present extensive simulation studies on the correctness of identification with the assumed pentagonal banded Toeplitz structure.

Alteration assemblage characterization using machine learning applied to high-resolution drill-core images, hyperspectral data and geochemistry

Integration of multiple data types is beneficial for prediction of geological characteristics. From the perspective that geochemistry characterizes the composition of a rock mass, hyperspectral data characterizes alteration mineralogy and image feature extraction characterizes texture, most geological classifications would be well-informed by the combination of these three features. The process of meaningfully integrating distinctly sourced datasets and producing scale-relevant predictions for geological classifications involves several steps. We demonstrate a workflow to comprehensively structure and integrate these three feature families, refine training data, predict alteration classes and mitigate noise derived from scale mismatch in output predictions. The dataset, compiled from the Josemaria porphyry copper–gold deposit in Argentina, is comprised of more than 14 000 intervals of approximately 2 m, taken from 36 drillholes, where geochemistry was merged with hyperspectral mineralogy represented as tabular pixel abundances, and textural metrics extracted from core imagery, structured into the geochemical interval. Feature engineering and principal component analysis provided insights into the behaviour of the ore system during intermediate steps, as well as providing uncorrelated feature inputs for a random forest predictor. Training data were refined by producing an initial prediction, thresholding the predictions to >70% dominant class probability and using those (high-probability) samples to produce a final model encoding better constrained separation between alteration assemblages. Prediction using the final model returned an accuracy of 82.5%, as a function of model discrepancy combined with logging ambiguity and a scale mismatch between generalized logged intervals and much more granular (2 m) feature inputs. Noise reduction and generalization to the desired resolution of output was achieved by applying the multiscale multivariate continuous wavelet transform tessellation method to class membership probabilities. Ultimately, a large database of logged drill core was homogenized using empirical methodologies. The described workflow is adaptable to distinct scenarios with some modification and is apt for integrating multiple input feature types and using them to systematically define geological classifications in drillhole data.

Low-dose IL-2 Treatment Rescues Cognitive Deficits by Repairing the Imbalance Between Treg and Th17 Cells at the Middle Alzheimer's Disease Stage.

Multiple studies highlight the role of effector and regulatory CD4+T cells in the pathophysiology of Alzheimer's disease, and foster low-dose IL-2 treatment which induces regulatory CD4+T (Treg) cells expansion and activation as a promising strategy for its treatment. However, studies demonstrating discrepant Treg functions in AD have been reported. In addition, a compromised immune system associated with aging may substantially impact on these processes. Here, we report that there is an altered balance of activity between Treg cells and IL-17-producing helper T (Th17) cells in periphery and brain of APP/PS1 mice along the disease progression. A dramatic loss of the healthy balance of activity between Treg and Th17 cells was found at the middle disease stage. While peripheral low-dose recombinant human IL-2 administration could selectively modulate the abundance of Treg cells and repair the imbalance between Treg and Th17 subsets at the middle disease stage. We further show that modulation of peripheral immune balance through low-dose IL-2 treatment reduces the neuro-inflammation and increases numbers of plaque-associated microglia, accompanied by marked reduction of Aβ plaque deposition and slower cognitive declines in APP/PS1 mice at the middle disease stage. Our study highlights the therapeutic potential of repurposed IL-2 for innovative immunotherapy based on modulation of the homeostasis of CD4+T cell subsets in Alzheimer's disease at the middle disease stage.

Cultural Adaptation and Validation of the Attitudes Towards Alcohol Scale for Health Professionals in Portugal

AbstractWe aimed to conduct an adaptation and validation of the Attitudes towards Alcohol, Alcoholism and Individuals with Alcohol Use Disorders Scale (EAFAA) which set to address the main groups of attitudes. It is composed in its original version by 50 items, divided into 4 factors: (1) work and interpersonal relationships with patients with alcohol consumption disorders; (2) the person with alcohol consumption disorders; (3) alcoholism (aetiology) and (4) alcoholic beverages and their use. The cultural adaptation from Brazilian to European Portuguese changes 22 items and the designation of one factor. The content validity index scale was 0.96. The psychometric properties were analysed through a cross-sectional study, with a convenience sample of 500 health professionals and students. The confirmatory factor analysis model with the same four (4) original factors, led to the elimination of 9 items. The scale revealed Cronbach’s alpha of 0.801. Empirical adjustment indices were satisfactory with Minimum Discrepancy Function by Degrees of Freedom divided (CMIN) (X2/gl) = 3.91; p < 0001, RMSEA = 0.076 and the goodness of fit index (GFI) with 0.8. Relative indices values were normal fit index (NFI) = 0.6, comparative fit index (CFI) = 0.7 and Tucker-Lewis index (TLI) = 0.7, with best parsimony indices PCFI = 0.595, PNFI = 0.530 and PGFI = 0.702.