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Abstract Background: Dyslipidemia and distorted fatty acid (FA) metabolism are frequent biochemical abnormalities associated with anorexia nervosa (AN). Gut microbiota is supposed to play an important…

Abstract Background: Dyslipidemia and distorted fatty acid (FA) metabolism are frequent biochemical abnormalities associated with anorexia nervosa (AN). Gut microbiota is supposed to play an important role in the etiopathogenesis of AN. Apart from the digestive function of bile acids (BAs), these compounds have multiple metabolic functions due to the activation of specific receptors. Objective/aims: The aims of the study were to investigate biochemical measures, including plasma lipids (lipoproteins, respectively), fatty acid (FA) patterns, and the profile of plasma Bas, in AN patients and healthy controls (CON). Methods: Plasma phospholipid FA and BAs profiles were analyzed in 39 women with a restrictive type of AN (AN-R; median age 17 years) and in 35 CON women (median age 20 years). Results: Compared to CON, AN had an increased concentration of HDL-C, increased content of palmitic acid, and decreased proportion of linoleic acid. Moreover, AN had a drop in the level of the sum of PUFAn-6 and increased delta 9 desaturase activity for stearic acid. In AN, we found decreased levels of plasma tauroursodeoxycholic acid (TUDCA). In AN, concentrations of 22:5n-6, 16:0, 20:3n-6 and fat mass index were predic-tors of HDL-C levels (R2 = 0.43). Conclusions: Patients with AN-R had an increased concentration of HDL-C, decreased levels of total PUFA n-6, and increased activity of D9D for stearic acid. Furthermore, AN exerted decreased levels of TUDCA. Therefore, a decreased level of TUDCA could potentially serve as a marker of AN.

Generalizovaná úzkostná porucha (GAD) se vyznačuje chronickými a nadměrnými obavami v různých aspektech každodenního života, včetně osobních a p…

Generalizovaná úzkostná porucha (GAD) se vyznačuje chronickými a nadměrnými obavami v různých aspektech každodenního života, včetně osobních a pracovních povinností, zdraví či mezilidských vztahů. Diagnostická kritéria procházejí mírnou úpravou při přechodu Mezinárodní klasifikace nemocí z verze 10 (MKN-10) na novou verzi, MKN-11. V našem článku se podíváme na změny v nové klasifikaci nemocí, která by se měla stát diagnostickým vodítkem v nadcházejících letech. Cílem tohoto článku je podrobněji prozkoumat charakteristiky generalizované úzkostné poruchy, její diagnostické výzvy a aktuální postupy v léčbě, včetně nejmodernějších přístupů. Léčba GAD obvykle zahrnuje farmakoterapii, psychoterapii nebo jejich kombinaci. Častými komplikacemi úspěšné léčby jsou vedlejší účinky léků nebo nedostatečná odpověď na léčbu až rezistence. Nadějí pro pacienty může být využití moderních technologií v léčbě generalizované úzkostné poruchy, jakými je například virtuální realita, kterou využíváme v našem centru Výzkumu virtuální reality v duševním zdraví a neurovědách v Národním ústavu duševního zdraví při léčbě úzkostných poruch, a to včetně GAD.   Generalized Anxiety Disorder (GAD) is characterized by chronic and excessive worries across various aspects of daily life, including personal and work-related responsibilities, health, and interpersonal relationships. The diagnostic criteria undergo a slight modification with the transition from the 10th edition of the International classification of diseases (ICD-10) to the new version, ICD-11. This article examines the changes in the new classification, which is expected to become the diagnostic guideline in the coming years. The aim of this article is to explore in detail the characteristics of generalized anxiety disorder, its diagnostic challenges, and current treatment approaches, including the most modern techniques. GAD treatment typically involves pharmacotherapy, psychotherapy, or a combination of both. Common complications in the successful treatment of GAD include side effects of medications or insufficient response to treatment, including resistance. A promising approach for patients may be the use of modern technologies in the treatment of generalized anxiety disorder, such as virtual reality used at our Virtual reality research center for mental health and neurosciences at the National institute of mental health in the treatment of anxiety disorders, including GAD.

Abstract Aristotle's Square also known as Square of Opposition, is a mathematical diagram dating back to Greek philosophy and exhibiting the connection between four logical propositions in a…

Abstract Aristotle's Square also known as Square of Opposition, is a mathematical diagram dating back to Greek philosophy and exhibiting the connection between four logical propositions in a simple graphical form. Fuzzy Relational Concept Analysis (FRCA) is a technique for extracting special clusters called fuzzy concepts from a Fuzzy Relational Context Family (FRCF), which is a dataset organized as multiple fuzzy object-attribute and object-object relations. The primary FRCA tools to obtain information from data are special fuzzy quantifiers viewed as interpretations in a model of formulas of the formal theory of the intermediate generalized quantifiers. This work focuses on the issue of generating a collection of fuzzy concepts from a certain FRCF, by choosing one of four particular FRCA quantifiers: the positive universal quantifier 𝒮1, the negative universal quantifier 𝒮−1, the positive existential quantifier 𝒮∃, and the negative existential quantifier 𝒮−∃. Certainly, the selection of the quantifier is crucial in the FRCA procedure since it affects the final concept classification: diverse fuzzy concepts arise from varying quantifiers. As the initial objective, this article introduces the logical relations involving 𝒮1,𝒮−1, 𝒮∃, and 𝒮−∃, in order to arrange them in a graded version of the Aristotelian square. The second goal of this study is to examine the connections among fuzzy concepts produced by distinct quantifiers in {𝒮1,𝒮−1,𝒮∃,𝒮−∃}. Therefore, our findings provide a twofold contribution to the advancement of Aristotle's square. Indeed, they reveal a novel interpretation of the square of opposition within the framework of Fuzzy Relational Concept Analysis, emphasizing its potential as a valuable tool for the analysis of data.

Computing maximum likelihood estimates in linear structural equation models is generally a difficult problem. The critical equations are usually non-linear and have numerous solutions, even for purely…

Computing maximum likelihood estimates in linear structural equation models is generally a difficult problem. The critical equations are usually non-linear and have numerous solutions, even for purely observational data. The block-coordinate descent (BCD) algorithm proposed by Drton et al. (2019)[1] is an efficient way to solve the optimization problem by decomposing it into a series of sub-problems with closed-form solutions, and which works with observational data. In this work, we describe the general problem of a BCD-type scheme for computing maximum likelihood estimates in linear structural equation models without hidden variables, integrating multiple observational and interventional environments. With interventional data, the degrees of both the original likelihood equations and the block-coordinate update equations could increase greatly. We study special setups in which the block optimization subproblems have a degree of at most 2 and provide closed-form solutions in these cases. Additionally, we discuss the potential applications of the model and algorithm to health and well-being data.

The data come from cross-sectional surveys conducted on representative samples of the non-institutionalised adult population in the Czech Republic in 2017, 2019 and 2022. The data include basic demogr…

The data come from cross-sectional surveys conducted on representative samples of the non-institutionalised adult population in the Czech Republic in 2017, 2019 and 2022. The data include basic demographic data and data from four questionnaires.  Data on mental health problems were assessed using the Mini International Neuropsychiatric Interview (M.I.N.I.) in 2017 and 2022. The Self-identification of Mental Illness (SELF-I) scale was used to assess self-identification as having a mental illness in these years. Stigma associated with mental health was assessed using the Reported and Intended Behaviour Scale (RIBS) and the Community Attitudes towards Mental Illness (CAMI) scale in 2019 and 2022.   Data pocházejí z průřezových šetření provedených na reprezentativních vzorcích neinstitucionalizované dospělé populace v České republice v letech 2017, 2019 a 2022. Data zahrnují základní demografické údaje a údaje ze čtyř dotazníků. Údaje o problémech v oblasti duševního zdraví byly hodnoceny pomocí Mini mezinárodního neuropsychiatrického rozhovoru (M.I.N.I.) v letech 2017 a 2022. K posouzení sebeidentifikace jako osoby s duševním onemocněním byla v těchto letech použita škála Self-identification of Mental Illness (SELF-I). Stigmatizace spojená s duševním zdravím byla hodnocena pomocí škály Reported and Intended Behaviour Scale (RIBS) a škály Community Attitudes towards Mental Illness (CAMI) v letech 2019 a 2022.

This paper examines the approximation capabilities of lattice integral transforms and their compositions in reconstructing lattice-valued functions. By introducing an integral kernel 𝑄 on the function…

This paper examines the approximation capabilities of lattice integral transforms and their compositions in reconstructing lattice-valued functions. By introducing an integral kernel 𝑄 on the function domain, we define the concept of a 𝑄-inverse integral kernel, which generalizes the traditional inverse kernel defined as a transposed integral kernel. Leveraging these 𝑄-inverses, we establish upper and lower bounds for a transformed version of the original function induced by the integral kernel 𝑄. The quality of approximation is analyzed using a lattice-based modulus of continuity, specifically designed for functions valued in complete residuated lattices. Additionally, under specific conditions, we demonstrate that the approximation quality for extensional functions with respect to the kernel 𝑄 can be estimated through the integral of the square of 𝑄, and in certain cases, these extensional functions can be perfectly reconstructed. The theoretical findings, illustrated through examples, provide a strong foundation for further theoretical advancement and practical applications.

A new concept, termed virtual neural networks, is introduced, where the count of trainable parameters is kept constant, and scalability is attained purely through computational resources. This concept…

A new concept, termed virtual neural networks, is introduced, where the count of trainable parameters is kept constant, and scalability is attained purely through computational resources. This concept is an abstract framework that can be realized using any standard convolutional neural network. It merges siamese neural networks with a deep ensemble technique by generating numerous virtual models that share weights derived from a small set of physical models. The ensemble comprises up to hundreds of trained models simultaneously. All virtual networks take the same input, and their interconnected structure induces an internal distortion that boosts the entire ensemble robustness. The accuracy of the ensemble improves as the number of virtual networks increases, without changing the capacity. Virtual neural networks outperform larger capacity models, typical deep ensembles, and contemporary approaches like SWA and Masksembles. Additionally, the highest performing individual model from the ensemble surpasses other models trained individually, even those with a greater number of parameters.

Inter-rater reliability (IRR) is one of the commonly used tools for assessing the quality of ratings from multiple raters. However, applicant selection procedures based on ratings from multiple raters…

Inter-rater reliability (IRR) is one of the commonly used tools for assessing the quality of ratings from multiple raters. However, applicant selection procedures based on ratings from multiple raters usually result in a binary outcome - the applicant is either selected or not. This final outcome is not considered in IRR, which instead focuses on the ratings of the individual subjects or objects. We outline the connection between the ratings' measurement model (used for IRR) and a binary classification framework. We develop a simple way of approximating the probability of correctly selecting the best applicants which allows us to compute error probabilities of the selection procedure (i.e., false positive and false negative rate) or their lower bounds. We draw connections between the IRR and the binary classification metrics, showing that binary classification metrics depend solely on the IRR coefficient and proportion of selected applicants. We assess the performance of the approximation in a simulation study and apply it in an example comparing the reliability of multiple grant peer review selection procedures. We also discuss other possible uses of the explored connections in other contexts, such as educational testing, psychological assessment, and health-related measurement, and implement the computations in the R package IRR2FPR.