Super-resolution technology is important not only in bio-related fields but also in nanotechnology, particularly in the semiconductor industry, where fine patterning is required and super-resolution is essential. However, observing microstructures beyond the diffraction limit proposed by Abbe and Rayleigh is considered impossible because of diffraction in traditional optical microscopy ob-servation techniques. However, in recent years, it has been possible to observe microstructures beyond the Rayleigh criterion by analysing the phase distribution of light. This study investigated the physical reasons why phase analysis makes this new analysis technique possible using simula-tions. The results confirmed that the phase component of the zero-order diffracted light reflected from the microstructure and able to pass through the lens system contained the phase information related to the shape of the measured object. Analysis of this information demonstrates the possi-bility of realising super-resolution based on speckle interferometry.

In this study, we introduce the dynamics of a Hepatitis B virus (HBV) model with the class of asymptomatic carriers and conduct a comprehensive analysis to explore its theoretical aspects and examine the crossover effect within the HBV model. To investigate the crossover behavior of the operators, we divide the study interval into two subintervals. In the first interval, the classical derivative is employed to study the qualitative properties of the proposed system, while in the second interval, we utilize the ABC fractional differential operator. Consequently, the study is initiated using the piecewise Atangana-Baleanu derivative framework for the systems. The HBV model is then analyzed to determine the existence, Hyers-Ulam (HU) stability, and disease-free equilibrium point of the model. Moreover, we showcase the application of an Adams-type predictor-corrector (PC) technique for Atangana-Baleanu derivatives and an extended Adams-Bashforth-Moulton (ABM) method for Caputo derivatives through numerical results. Subsequently, we employ computational methods to numerically solve the models and visually present the obtained outcomes using different fractional-order values. This network is designed to provide more precise information for disease modeling, considering that communities often interact with one another, and the rate of disease spread is influenced by this factor.

In this paper, a robust nonlinear dynamic controller is designed for a wind turbine with a permanent magnet synchronous generator. The wind turbine is subject to variations in all the parameters appearing in its mathematical model. Furthermore, the wind velocity is considered unavailable for direct measurement. This situation is of particular interest in practical applications, where only the nominal parameter values are known, and accurate wind velocity measurement is challenging due to perturbations caused by the turbine itself, even with appropriate sensors. The problem addressed in this work involves tracking the reference angular velocity corresponding to the wind velocity. To achieve accurate tracking, appropriate compensation of the perturbation terms resulting from parameter uncertainties and wind estimation errors is required. To address this problem, an estimator of the wind velocity is utilized, along with high–order sliding mode parameter estimators, to ensure high–performance operation of the turbine.

The paper focuses on the admissibility problem of descriptor fractional-order systems (DFOSs). The alternate admissibility criteria are addressed for DFOSs with order in (0,2) which involve a non-strict linear matrix inequality (LMI) method and a strict LMI method, respectively. The forms of non-strict and strict LMIs are brand-new and distinguished with the existing literature, which fill the gaps of studies for admissibility. These approaches are available to the order in (0,2) without separating the order ranges into (0,1) and [1,2). In addition, a method involved least real decision variables in terms of strict LMIs is derived which is more convenient to process the practical solution. Three numerical examples are given to illustrate the validity of proposed results.

The continuing public health issue known as COVID-19 (the 2019 Novel Corona virus infection) has a global emphasis. Despite (or perhaps because of) the fact that there are significant gaps in our understanding of COVID-19 epidemiology, transmission dynamics, research methods, and management breakout poses a new kind of global hazard. The good news is that there is currently enough knowledge about the epidemic process to allow for the creation of mathematical forecasting models. We modify a conventional SEIR epidemic model to the unique dynamic compartments and epidemic features of COVID 19 as it spreads in a population with a diverse age structure. Although many US states and other nations around the world followed lockdown and reopening processes, we perform some analysis on using some techniques of the epidemic course. A new perspective of fractional calculus known as piecewise derivatives of fractional order is used to study the proposed model. Sufficient conditions are established to show the existence theory. In addition, a numerical scheme based on Newton’s polynomials is established to simulate the approximate solutions of the proposed model by using various fractional orders. Some real data results are also shown with comparison of the numerical results.

Asymmetry in the upper and lower tails is an important feature in modeling bivariate distributions. This article focuses on the log ratio between the tail probabilities at upper and lower corners as a measure of tail asymmetry. Asymptotic behavior of this measure at extremely large and small thresholds is explored with particular emphasis on the skew-normal copula. Our numerical studies reveal that, when the correlation or skewness parameters are around at the boundary values, some asymptotic tail approximations of the skew-normal copulas proposed in the literature are not suitable to compute the measure of tail asymmetry with practically extremal thresholds.

In network analysis, links depict the connections between each pair of network nodes. However, such pairwise connections fail to consider the interactions among more agents, which may be indirectly connected. Such non-pairwise or higher-order connections can be signified by involving simplicial complexes. The higher-order connections become even more noteworthy when it comes to neuronal network synchronization, an emerging phenomenon responsible for the many biological processes in real-world phenomena. However, involving higher-order interactions may considerably increase the computational costs. To confound this issue, map-based models are more suitable since they are faster, simpler, more flexible, and computationally more optimal. Therefore, this paper addresses the impact of pairwise and non-pairwise neuronal interactions on the synchronization state of 10 coupled memristive Hindmarsh-Rose neuron maps. To this aim, electrical, inner linking, and chemical synaptic functions are considered as 2- and 3-body interactions in three homogenous and two non-homogenous cases. The results show that through chemical pairwise and non-pairwise synapses, the neurons achieve synchrony with the weakest coupling strengths.

We analyze different approaches to quantum gravity. It is stressed that nonperturbative methods to quantise gravity and the usage of diffeomorphism-invariant variables are very important. We pay attention on the Wheeler--DeWitt equation in the framework of canonical quantum gravity. The Wheeler--DeWitt equation is presented in the first order formalism with the hope that this form can solve some problems such as singularities and the ordering. Also, there is a problem of defining the time.

Studying concurrent systems is to sort the read/write and control events in the concurrent system according to the order, or in partial order, or in strict order. Because of the concurrency of events and the timeliness of writing events, there will be overlap between events, which requires interval order analysis. A single start and end sequence to represent the entire hierarchical order structure as well as all equivalent spaced order observations.a single sequence of beginnings and endings to represent the entire stratified order structures as well as all equivalent interval order observations. Mazurkiewicz traces and Comtracks are hierarchical traces, but they cannot describe interval traces. In a few sentences of this summary, we can tell the problems (petri interval traces with data), the previous work (Mazurkiewicz traces, Comtracks), the reasons that cannot be solved, and our own solutions (DPN: Petri nets with data, interval traces). This paper focuses on a BE (Beginnings and Endings) sequence representing an equivalent class of runs, or targets. Our goal is to have a BE sequence to represent the entire hierarchical order structure, that is, all equivalent observable interval orders.

Initial periods of adsorption kinetics play an important role in estimating the initial adsorption rate and rate constant of adsorption process. Several adsorption processes rapidly occur, and the experimental data of adsorption kinetics under the initial periods can contain potential errors. The pseudo-second-order (PSO) kinetic model has been popularly applied in the field of adsorption. The use of the nonlinear optimization method to obtain the parameters of the PSO model can minimize error functions during modelling compared to the linear method. However, the nonlinear method has limitations in that it cannot directly recognize potential errors in the experimental points of time-dependent adsorption, especially under the initial periods. In this study, for the first time, the different linear types (Types 1–6) of the PSO model are applied to discover the error points under the initial periods. The result indicated that the fitting method using its linear equations (Types 2–5) is really helpful for identifying the error (doubtful) experimental points from the initial periods of adsorption kinetics. The imprecise points lead to low adjusted R2 (adj-R2), high reduced χ2 (red-χ2), and high Bayesian information criterion (BIC) values. After removing those points, the experimental data were adequately fitted with the PSO model. statistical analyses demonstrated that the nonlinear method must be used for modelling the PSO model because its red-χ2 and BIC were lower than the linear method. Type 1 has been extensively applied in the literature because of its very high adj-R2 (0.9999) and its excellent fitting to experimental points. However, its application should be limited because the potential errors from experimental points were not identified by this type. For comparison, the other kinetic models (i.e., pseudo-first-order, pseudo-nth-order, Avrami, and Elovich) are applied. The modelling result using the nonlinear forms of those models indicated that the fault experimental points from the initial periods were not detected.

The article is dedicated to the role of polychrome solutions of the architectonic order in the concept of the Baroque façade. The ancient principles of designing architectural structures, inherited from the Renaissance were subjected to reinterpretations in order to impart different expressive values. The arrangements of façades, initially balanced or even horizontal, were replaced by ambiguous bivalent compositions. Vertical layouts began to dominate in the Baroque. Appropriately selected polychrome of the elements of the order could emphasize the compositional expression. The relationship between the layout of the polychrome in a given architectural order and the expression of a work of art has been established for quite a long time. However, the generally available data on color schemes of architectural structures in baroque buildings are still not fully organized. The paper analyses examples of Baroque façades preserved in their original state and revalorized in recent years after thorough conservator’s research in the field of architecture and color. The examples are mainly designed in the so-called great order, i.e. pertaining largely to church façades. In the Baroque, the vertical direction of the composition was strongly emphasized by multiplying or applying perspective arrangements of supports, and finally by embattled entablatures. The decisive field of change became the shaping of the coloristic decoration of the entablature – decisions regarding the material and color separation of elements of the frieze above the supports. The uniform color of all vertical elements of the façade structure guaranteed an unambiguous verticality of the composition.

In the study of materials and macromolecules by first-principle methods, the bond order is a useful tool to represent molecules, bulk materials and interfaces in terms of simple chemical concepts. Despite the availability of several methods to compute the bond order, most applications have been limited to small systems because a high spatial resolution of the wave function and an all-electron representation of the electron density are typically required. Both limitations are critical for large-scale atomistic calculations, even within approximate density-functional theory (DFT) approaches. In this work we describe our methodology to quickly compute delocalization indices for all atomic pairs, while keeping the same representation of the wave function used in most compute-intensive DFT calculations on high-performance computing equipments. We describe our implementation into a post-processing tool, designed to work with Quantum ESPRESSO, a popular open-source DFT package. In this way we recover a description in terms of covalent bonds from a representation of wave function containing no explicit information about atomic types and positions.

This note addresses a fundamental problem in matrix theory on establishing and characterizing range equalities for matrix expressions that involve generalized inverses. We first establish a group of necessary and sufficient conditions for the matrix range equality ${\rm range}(D_1 - C_1A_1^{\dag}B_1) = {\rm range}(D_2 - C_2A_2^{\dag}B_2)$ to hold, where $(\cdot)^{\dag}$ denotes the Moore--Penrose inverse of matrix. We then give several groups of range equalities with extrusion properties for multiple matrix products associated with two matrices and their conjugate transposes and Moore--Penrose inverses.

The effects of mixing orders of tannic acid (TA), starch and α-amylase on the enzyme inhibition of TA were studied, including mixing TA with α-amylase before starch addition (order 1), mixing TA with pre-gelatinized starch before α-amylase addition (order 2) and co-gelatinizing TA with starch before α-amylase addition (order 3). It was found that the enzyme inhibition was always highest for order 1 because TA could bind with the enzyme active site thoroughly before digestion occurred. Both order 2 and 3 reduced α-amylase inhibition through decreasing binding of TA with the enzyme, which resulted from the non-covalent physical adsorption of TA with gelatinized starch. Interestingly, at low TA concentration, α-amylase inhibition for order 2 was higher than order 3, while at high TA concentration, the inhibition was shown with opposite trend, which arose from the difference in the adsorption property between the pre-gelatinized and co-gelatinized starch at the corresponding TA concentrations. Besides, both the crystalline structures and apparent morphology of starch were not significantly altered by TA addition for order 2 and 3. Conclusively, although a polyphenol may have an acceptable inhibitory activity in vitro, the actual effect may not reach the expected one when taking processing procedures into account.

This note shows that the well-known reverse order law $(AB)^{\dag} = B^{\dag}A^{\dag}$ for the Moore--Penrose inverse of matrix product is equivalent to many other equalities for that are composed of multiple products $(AB)^{\dag}$ and $B^{\dag}A^{\dag}$ by means of the definition of the Moore--Penrose inverse and orthogonal projector theory.

The semi-logarithmic plot of the cumulative number of cases of epidemics resembles the response of a first-order systems for a step load. This similarity was utilized to develop a first order model that can be used for extracting information about the dynamics of infectious disease epidemics. The developed model was validated using COVID-19 data of China. It was also heuristically fitted to other 13 countries. Obtained results indicated that the model can reliably forecasts the number of infected person, epidemic growth speed towards steady-state condition (process time constant, T), and time to reach steady-state condition (4T). The developed model will help public health authorities in developing more effective control strategies of epidemics.

This study presents a prediction model based on Logistic Growth Curve to evaluate the effectiveness of Movement Control Order (MCO) on COVID-19 pandemic spread. The evaluation assesses and predicts the growth models. The estimated model is a forecast-based model that depended on partial data from the COVID-19 cases in Malaysia. The model is then studied together with the effectiveness of the three phases of MCO implemented in Malaysia. Evidence from this study suggests that results of the LGC prediction model match with the progress and effectiveness of the MCO to flatten the curve, thus helped to control the spike in number of active COVID-19 cases and spread of COVID-19 infection growth.

A premise of statistical learning research is that learners attend to and learn the frequencies of repeating or co-occurring elements in the input. When the input is a series of words, participants readily learn the frequencies of phoneme sequences, that is, to learn phonotactic frequencies. Inherent to the concepts of both frequency and phonotactics is order, or the temporal structure of the input. Order is similarly inherent to statistical learning, yet the effect of order on statistical learning is not well understood. In the present study, adult participants learned the relative frequencies of eight item-medial consonant sequences, for example, the /mk/ in /nʌmkət/. Across five ordering conditions, both familiarization and test stimuli were independently ordered and randomized, thus allowing for a relatively broad search for order effects in an established statistical learning paradigm. Participants learned the target frequencies equivalently across the five ordering conditions, indicating no modulating effect of order. Nevertheless, participants also approached the task by applying idiosyncratic, structured orders to their responses. The result is an unexpected but robust effect of order. Both the results and the design of the study also allow for increased integration of statistical learning with memory and other aspects of cognition.

In this effort we propose a data-driven learning framework for reduced order modeling of fluid dynamics. Designing accurate and efficient reduced order models for nonlinear fluid dynamic problems is challenging for many practical engineering applications. Classical projection-based model reduction methods generate reduced systems by projecting full-order differential operators into low-dimensional subspaces. However, these techniques usually lead to severe instabilities in the presence of highly nonlinear dynamics, which dramatically deteriorates the accuracy of the reduced-order models. In contrast, our new framework exploits linear multistep networks, based on implicit Adams-Moulton schemes, to construct the reduced system. The advantage is that the method optimally approximates the full order model in the low-dimensional space with a given supervised learning task. Moreover, our approach is non-intrusive, such that it can be applied to other complex nonlinear dynamical systems with sophisticated legacy codes. We demonstrate the performance of our method through the numerical simulation of a two-dimensional flow past a circular cylinder with Reynolds number Re = 100. The results reveal that the new data-driven model is significantly more accurate than standard projection-based approaches

Multiobjective optimization plays an increasingly important role in modern applications, where several criteria are often of equal importance. The task in multiobjective optimization and multiobjective optimal control is therefore to compute the set of optimal compromises (the Pareto set) between the conflicting objectives. The advances in algorithms and the increasing interest in Pareto optimal solutions have led to a wide range of new applications related to optimal and feedback control which results in new challenges such as expensive models or real-time applicability. Since the Pareto set generally consists of an infinite number of solutions, the computational effort can quickly become challenging which is particularly problematic when the objectives are costly to evaluate or when a solution has to be presented very quickly. This article gives an overview over recent developments in accelerating multiobjective optimal control for complex problems where either PDE constraints are present or where a feedback behavior has to be achieved. In the first case, surrogate models yield significant speed-ups. Besides classical meta-modeling techniques for multiobjective optimization, a promising alternative for control problems is to introduce a surrogate model for the system dynamics. In the case of real-time requirements, various promising model predictive control approaches have been proposed, using either fast online solvers or offline-online decomposition. We also briefly comment on dimension reduction in many-objective optimization problems as another technique for reducing the numerical effort.

We monitored the water quality and hydrological conditions of a horizontal subsurface constructed wetland (HSSF-CW) in Beijing, China, for 2 years. We simulated the area rate constant and the temperature coefficient with the first-order kinetic model. We examined the relationships between the nitrogen (N) removal rate, N load, seasonal variations in the N removal rate, and environmental factors, such as the area rate constant, temperature, and dissolved oxygen (DO). The effluent ammonia (NH₄⁺-N) and nitrate (NO₃⁻-N) concentrations were significantly lower than the influent concentrations (p<0.01, n=38). The NO₃⁻-N load was significantly correlated with the removal rate (R²=0.9566, p<0.01), but the NH^₄+-N load was not correlated with the removal rate (R²=0.0187, p>0.01). The area rate constants of NO₃⁻-N and NH₄⁺-N at 20 °C were 27.01±26.49 and 16.63±10.58 m∙yr⁻¹, respectively. The temperature coefficients for NO₃⁻-N and NH₄⁺-N were estimated at 1.0042 and 0.9604, respectively. The area rate constants for NO₃⁻-N and NH₄⁺-N were not correlated with temperature (p>0.01). The NO₃⁻-N area rate constant was correlated with the corresponding load (R²=0.9625, p<0.01). The NH₄⁺-N area rate was correlated with DO (R²=0.6922, p<0.01), suggesting that the factors that influenced the N removal rate in this wetland met Liebig's law of the minimum.

The classical propositional calculus (zero-order logic, classical propositional logic), is the most fundamental two-valued logical system. In this paper we present a proof of inconsistency of the classical propositional calculus. Then, we get right away the conclusion that the metatheory of the classical propositional calculus is inconsistent.

A reduced order model is developed to monitor aeroengines condition (defining their degradation from a baseline state) in real-time, by using data collected in specific sensors. This reduced model is constructed by applying higher order singular value decomposition plus interpolation to appropriate data, organized in tensor form. Such data are obtained using a detailed engine model that takes the engine physics into account. Thus, the method synergically combines the advantages of data-driven (fast online operation) and model-based (the engine physics is accounted for) condition monitoring methods. Using this reduced order model as surrogate of the engine model, two gradient-like condition monitoring tools are constructed. The first tool is extremely fast and able to precisely compute `on the fly’ the turbine inlet temperature, which is a paramount parameter for the engine performance, operation, and maintenance, and can only be roughly estimated by the engine instrumentation in civil aviation. The second tool is not so fast (but still reasonably inexpensive) and precisely computes both, the engine degradation and the turbine inlet temperature at which sensors data have been acquired. These tools are robust in connection with random noise added to the sensors data and can be straight forwardly applied to other mechanical systems.

This paper presents an approach to the Euler-Bernoulli beam theory (EBBT) using the finite difference method (FDM). The EBBT covers the case of small deflections, and shear deformations are not considered. The FDM is an approximate method for solving problems described with differential equations. The FDM does not involve solving differential equations; equations are formulated with values at selected points of the structure. Generally, the finite difference approximations are derived based on fourth-order polynomial hypothesis (FOPH) and second-order polynomial hypothesis (SOPH) for the deflection curve; the FOPH is made for the fourth and third derivative of the deflection curve while the SOPH is made for its second and first derivative. In addition, the boundary conditions and not the governing equations are applied at the beam’s ends. In this paper, the FOPH was made for all of the derivatives of the deflection curve, and additional points were introduced at the beam’s ends and positions of discontinuity (concentrated loads or moments, supports, hinges, springs, etc.). The introduction of additional points allowed us to apply the governing equations at the beam’s ends and to satisfy the boundary and continuity conditions. Moreover, grid points with variable spacing were also considered, the grid being uniform within beam segments. First-order analysis, second-order analysis, and vibration analysis of structures were conducted with this model. Furthermore, tapered beams were analyzed (element stiffness matrix, second-order analysis). Finally, a direct time integration method (DTIM) was presented. The FDM-based DTIM enabled the analysis of forced vibration of structures, with damping taken into account. The results obtained in this paper showed good agreement with those of other studies, and the accuracy was increased through a grid refinement. Especially in the first-order analysis of uniform beams, the results were exact for uniformly distributed and concentrated loads regardless of the grid. Further research will be needed to investigate polynomial refinements (higher-order polynomials such as fifth-order, sixth-order…) of the deflection curve; the polynomial refinements aimed to increase the accuracy, whereby non-centered finite difference approximations at beam’s ends and positions of discontinuity would be used.

Causation concepts for work-related musculoskeletal disorders vary among authors and academic disciplines. Major causation concepts are single-event and repetitive motion. The aim of this short communication is to share findings from a recent survey, conducted by the U.S. Bureau of Labor Statistics of United States employers about work-related musculoskeletal cases, and more specifically, about whether the employers regard the cause of their employee’s musculoskeletal problem as being from a single exposure or from multiple exposures. Recommendations are offered for using terminology consistent with employer understandings.

The prevalence of patients on buprenorphine therapy presenting for elective surgery has increased. Buprenorphine is a widely used medication for management of patients with chronic pain. It is also used for maintenance therapy for patients with a history of opioid use disorder (OUD). Due to lack of standardized protocol for managing patients on buprenorphine peri-operatively, we performed a retrospective analysis to compare pain score outcomes and post-operative opiate requirements between patients who continued versus discontinued buprenorphine. We identified 35 patients, 11 continued buprenorphine and 24 discontinued buprenorphine. The average Post-Anesthesia Care Unit (PACU) pain score was 7.54 for those who discontinued pre-operative buprenorphine and 5.59 for those who continued (P value 0.0339). The average post-operative Morphine Milligram Equivalent (MME) use was 86.13 for those who continued pre-operative buprenorphine and was 107.70 for those who discontinued buprenorphine (P value 0.6439). The results from our study correlate with several previous studies which showed lower PACU pain scores in patients who continued buprenorphine. There is a benefit of decreased pain post-operatively when pre-operative buprenorphine is continued and having a decrease in relapse in those with a history of OUD.

Temporomandibular disorders (TMDs) refer to a group of conditions that affect the temporomandibular joint, causing pain and dysfunction in the jaw joint and related muscles. The diagnosis of TMDs typically involved clinical assessment through operator-based physical examination, self-reported questionnaire and imaging studies. To objectivize the measurement of TMD, this study aims at investigating on the feasibility to use machine-learning algorithms fed with data gathered from low-cost and portable instruments to discriminate the presence of TMD in adult subjects. Through this aim, the experimental protocol involved fifty participants, equally distributed between TMD and healthy subjects, acting as control group. The diagnosis of TMD has been performed by a skilled operator through the typical clinical scale. Participants underwent to a baropodometric analysis by using a pressure matrix and the evaluation of the cervical mobility through inertial sensors. Nine machine-learning algorithms belonging to support vector machine, k-nearest neighbours and decision tree were compared. The k-nearest neighbours based on cosine distance was found as the best performing, achieving performance of 0.94, 0.94 and 0.08 for the accuracy, F1-score and G-index, respectively. These findings open the possibility to use such methodology to support the diagnosis of TMDs in clinical environments.

Patients with a psychotic disorder frequently report oral health problems, while mental health nurses (MHNs) seem not to be fully aware of these problems and the risk factors. Therefore, this study aims to develop supportive tools for MHNs regarding oral health to increase sensitization among MHNs and to provide MHNs with knowledge to recognize (potential) oral health problems in patients with a psychotic disorder. We used a human-centered design in which the user, MHNs as well as experts by experience, were placed in the centre of the research process. Problems and needs in MHNs working with patients with a psychotic disorder were addressed. To identify key issues of problem as well as the needs in terms of resources, we started with focus groups (n = 9). We analysed the data thematically based on context of patients and MHNs in practice regarding oral health, preferred oral health tools focused on MHN, and intended outcomes of tools. A Multi Criteria Decision Matrix was developed and analysed (n = 9) to identify the most optimal and viable solution based on established criteria and issues that are prevalent in the work of MHNs. Most promising result was the development of a brochure with an awareness screener. The brochure with awareness screener was developed as a low-fidelity prototype for MHNs regarding oral health in patients with a psychotic disorder, based on the latest scientific evidence. After testing it, the feasibility was tested through semi-structured interviews (n=19). MHNs and experts by experience were satisfied with the tool and provided recommendations for adjustments to the content. Significant augmentations to the brochure include a clinical lesson and a toothbrush with toothpaste for patients. We can conclude that a brochure with an awareness screener is feasible for MHNs. Future steps aiming to further refine and optimize care processes for oral health tools in MHNs, should take refining eligibility criteria for psychiatric populations and the language level of the target group of a tool into account.

To explore the relevant factors that affect the efficacy of consciousness-regaining therapy (CRT)for prolonged disorder of consciousness(pDOC),a retrospective analysis was conducted on the case data of 114 patients with pDOC admitted to department of functional neurosurgery of Tianjin Huanhu Hospital from January 2019 to January 2022. Basic information on the cases,data on pDOC disease assessment, consciousness-regaining therapy methods, and efficacy evaluation were collected. A total of 114 patients were grouped and compared based on the efficacy at the end of treatment. Among them, there were 61 cases in the ineffective group and 53 cases in the effective group. There was no statistically significant difference (P&gt;0.05) between the two groups in terms of gender, age, etiology, acute cerebral herniation, emergency craniotomy surgery, emergency decompressive craniectomy, time from onset to start of consciousness-regaining therapy , and duration of consciousness-regaining therapy(P&gt;0.05). However, there were statistically significant differences (P&lt;0.05) in terms of secondary hydrocephalus, , consciousness-regaining therapy methods, CRS-R grading before treatment, and GOSE score at 6 months after treatment. Binary logistic regression analysis showed that the type of therapy (OR=0.169, 95% CI: 0.057-0.508) was a related factor affecting the efficacy of consciousness-regaining therapy (P&lt;0.05).Compared with non-invasive therapy, personalized awakening therapy using various invasive consciousness-regaining therapy methods can help improve the efficacy of therapy for pDOC.

Advanced paternal age increases the risk of transmitting de novo germline mutations, particularly missense mutations activating the receptor tyrosine kinase (RTK) signaling pathway, exemplified by the FGFR3 mutation linked to achondroplasia (ACH). This risk is attributed to the expansion of spermatogonial stem cells carrying the mutation, forming sub-clonal clusters in the ageing testis, thereby increasing the frequency of mutant sperm and the number of affected offspring from older fathers. While prior studies proposed a correlation between sub-clonal cluster expansion in the testis and elevated mutant sperm production in older donors, limited data exist on the universality of this phenomenon. Our study addresses this gap by examining the testis expansion patterns, as well as increases of mutations in sperm for two FGFR3 variants: c.1138G>A (p.G380R) and c.1948A>G (p.K650E)- associated with ACH or thanatophoric dysplasia (TDII), respectively. Unlike the ACH mutation, which showed sub-clonal expansion events in an aged testis and a significant increase of mutant sperm with the donor’s age, the TDII mutation formed sub-clonal clusters, but exhibited reduced transmission into sperm and no significant age-related increase. The mechanism behind this divergence remains unclear, suggesting potential pleiotropic effects of aberrant RTK signaling in the male germline, possibly hindering differentiation requiring meiosis. This study provides further insights into transmission risks of micro-mosaics associated with advanced paternal age in the male germline.

Reactive attachment disorder (RAD) and disinhibited social engagement disorder (DSED) manifest in individuals facing attachment system chal-lenges, particularly observed in minors under protective measures. The lack of standardized tools for assessing these difficulties and uncertainty about the most effective instruments from a psychometric perspective prompted this study. Using the COSMIN checklist, we systematically reviewed instruments assessing RAD, adhering to PRISMA and COSMIN guidelines. Examined tools included the Disturbance Attachment Inter-view, Preschool Age Psychiatric Assessment, Relationship Patterns Questionnaire, Assessment of RAD and DSED, Development and Well-Being Assessment, and Reactive Attachment Disorder Question-naire. Of the 11 articles analyzed, results highlight a research emphasis on internal consistency, structural and construct validity, sidelining other properties. While the instruments demonstrate strong structural validity, excellent internal consistency, and generally satisfactory error measurement results, the study concludes a dearth of investigations into psychometric properties for children and adolescents under protective measures.

Sleep disorders are a frequent problem in pediatrics. The prevalence is variable (from 15 to 50%) although the majority of papers coincide in that 1 every 3 kids can experiment some kind of sleep disorder. Underlying medical problems usually manifest by difficulty to fall asleep or stay asleep. The diagnosis and resolution of the medical problems with the specific treatment should be the prime concern, before starting with the treatment of the sleep behavioral problem

Adult patients with Autism Spectrum Disorder (ASD) exhibit a range of behaviours that can be disruptive to the medical care of themselves and other patients and as a result, are at higher risk of being sedated. The aim of the present study was to determine whether patients with ASD presenting to hospital for acute psychiatric crises re-ceive more sedative medications compared to similar patients without ASD. 43 adult patients with a previous di-agnosis of ASD who were referred to the mental health team at a single, large emergency department in metro-politan Victoria over the year of 2021 were identified and matched with an equal number of controls within the same cohort. Sedative medications were converted to diazepam and chlorpromazine equivalent doses and com-pared using repeated measures T-tests. Patients with ASD received 5.1mg more diazepam-equivalent benzodiaz-epines on average compared to matched controls (95%CI: 0.41-9.71). This difference was statistically significant (T=2.20, df=42, p=0.034). There was no significance difference in chlorpromazine-equivalent antipsychotic doses. Clinicians should be aware of the higher dose of sedatives, and consider other means of behavioural management in these patients.

Autism Spectrum Disorder (ASD), a neurodevelopmental disorder characterized by persistent deficits in social interaction and communication manifests in early childhood and is followed by restricted and stereotyped behaviors, interests or activities in adolescence and adulthood (DSM-V). Although genetics and environmental factors have been implicated, the exact causes of ASD have yet to be fully characterized. New evidence suggests that dysbiosis or perturbation in gut microbiota (GM) and exposure to lead (Pb) may play important roles in ASD etiology. Pb is a toxic heavy metal that has been linked to a wide range of negative health outcomes including anemia, encephalopathy, gastroenteric diseases and more importantly cognitive and behavioral problems inherent to ASD. Pb exposure can disrupt GM, which is essential for maintaining overall health. GM, consisting of trillions of microorganisms, has been shown to play a crucial role in the development of various physiological and psychological functions. GM interacts with the brain in a bidirectional manner referred to as “Gut-Brain Axis (GBA).” In this review, following a general overview of ASD and GM, the interaction of Pb with GM in the context of ASD is emphasized. Potential exploitation of this interaction for therapeutic purposes is also touched upon.

Introduction: Mucopolysaccharidosis type I (MPSI) is an autosomal recessive lysosomal storage disorder characterized by deficient or absent α-L-iduronidase (IDUA) enzyme activity due to pathogenic variants in IDUA gene. Early treatment with hematopoietic stem cell transplantation and/or enzyme replacement therapy is associated with improved outcomes in this progressive multisystem disease. The diagnosis is usually delayed due to late presentation and nonspecific symptoms resulting in high morbidity and mortality. The incidence of MPSI in US is estimated to be 0.26:100,000, however, it is unknown in Kuwait. This pilot study was undertaken to screen MPSI in all Kuwaiti neonates born at Farwaniya Hospital over a period of 12-months. This study examined the incidence of MPSI in a major center in Kuwait for inclusion in the national newborn screening program to enable its early detection and adequate treatment. Methods: All Kuwaiti neonates born at Farwaniya Hospital, Kuwait from December 2021 to December 2022, were screened for MPSI. The screening consisted of determining IDUA enzyme activity in dried blood spots (DBS)-derived samples by Tandem Mass Spectrometry. A follow-up genetic analysis of IDUA gene is planned to screen the cases with diminished IDUA enzyme activity as second-tier testing. Results: A total of 618 newborns, including 331 (54%) males and 287 (46%) females, were screened. Twenty of them had deficient IDUA enzyme activity but showed negative genetic testing for IDUA. However, we have diagnosed one additional female baby with MPSI, who belonged to Farwaniya Hospital, but the parents chose to deliver in a private hospital. She presented at age three months with recurrent upper airway infections, snoring and extensive Mongolian spots. The molecular study revealed previously reported pathogenic nonsense variant in IDUA c.1882C>T; p.(Arg628Ter), associated with severe phenotype. That being included, MPSI is estimated to be about 0.3% among tested females and 0.2% of all screened cases in Kuwait. Conclusion: Our study is the first to evaluate the incidence of MPSI in Kuwait. Given the single center, small number of screened babies and the short study duration thus far, it is premature to calculate the incidence of MPSI. It is anticipated that as the study continues and more infants are examined, we would be able to estimate the incidence of this disease in our population correctly. Further studies including screening newborns in all maternity hospitals in Kuwait are needed to calculate the actual incidence of MPSI. Our data supports including MPSI in national newborn screening program to allow early initiation of treatment and thus improve disease outcome.

The purpose of this systematic review is to examine the various articles published in the field of neuropsychology of anxiety research in terms of publication type and volume, methodology, and subject matter. The significance of this topic derives from the clinical and functional consequences of cognitive symptoms. Generalized anxiety disorder refers to the cluster of clinical symptoms characterized by predominant anxious anticipation and difficulty controlling worry. In order for the review to be systematic and reproducible, a search of the PsycINFO, Scopus, PubMed, Elsevier databases and an analysis using the PRISMA method are suggested. After analyzing the research data, empirical studies evaluating the clinical neuropsychology of generalized anxiety disorder are discussed.

The Speer allergic tension-fatigue syndrome (SATFS) is a classic syndrome characterized by allergy-like symptoms, muscle tension, headache, chronic fatigue, and a particular behavior. The particular behavior displayed includes symptoms such as hyperkinesis, hyperesthesia (i.e., insomnia), restlessness, and distractibility, among others. Interestingly, these symptoms are very similar to recent descriptions of attention deficit hyperactivity disorder (ADHD), the most prevalent neurodevelopmental disorder worldwide, which is characterized by inattention, hyperactivity and impulsivity. Although the exact cause of ADHD remains unknown, it has been proposed that deficiency of the enzyme diamine oxidase (DAO), which metabolizes histamine, may be involved in the development of ADHD. Our conceptual paper suggests that DAO enzyme deficiency may be involved in the development and severity of ADHD. Histamine, which is metabolized by DAO, plays an important role in the regulation of attention, memory and cognition. Therefore, decreased DAO activity could lead to an accumulation of histamine, which could contribute to ADHD symptoms. This study provides a theoretical framework for the relationship between DAO deficiency and ADHD, which could have important implications for the diagnosis and treatment of the disorder. Further empirical studies are needed to confirm our hypothesis and explore the clinical implications of the relationship between DAO deficiency, histamine, and ADHD.

In recent years, there has been a rise in the prevalence of autism spectrum disorder (ASD). The diagnosis of ASD requires behavioral observation and standardized testing completed by highly trained experts. Early intervention for ASD can begin as early as 1-2 years of age, but ASD diagnoses are not typically made until ages 2-5 years, thus delaying the start of intervention. There is an urgent need for non-invasive biomarkers to detect ASD in infancy. While previous research using physiological recordings has focused on brain-based biomarkers of ASD, this study investigated the potential of electrocardiogram (ECG) recordings as an ASD biomarker in 3-6-month-old infants. We recorded heart activity of infants at typical and elevated familial likelihood for ASD during naturalistic interactions with objects and caregivers. After obtaining the ECG signals, features such as heart rate variability (HRV) and sympathetic and parasympathetic activities were extracted from them. Then we evaluated the effectiveness of multiple machine learning classifiers for the classification of ASD likelihood. Our findings support our hypothesis that infant ECG signals contain a significant amount of information about ASD familial likelihood. Among the various machine learning algorithms tested, XGBoost performed best according to sensitivity (0.76±0.12), f1-score (0.75±0.12), precision (0.79±0. 12), classification accuracy (0.77±0.12, p-value = 0.01) and AUC (0.76±0.12, p-value = 0.02). These results suggest that ECG signals contains relevant information about the likelihood of an infant to develop ASD. Future studies should consider the potential of information contained in ECG, and other indices of autonomic control, for the development of biomarkers of ASD in infancy.

Background and Objectives: Light therapy (LT) is used as an adjunctive treatment for sleep problems. This study evaluates the impact of LT on sleep quality and sleep-related parameters in patients with sleep disorders. Materials and Methods: We performed a pilot randomized, open-label clinical trial. Fourteen patients aged 20–60 years with sleep disorders for more than three months were randomized into the control and LT groups (1:1 ratio). The LT group was instructed to use a device that provides bright LT (6000 K, 380 lux, wavelength 480 nm) for at least 25 minutes before 09:00 am for two weeks. A self-reported questionnaire was used to evaluate circadian preference, mood, and sleep-related parameters. We analyzed serum cortisol levels and clock genes expression. Results: The Epworth Sleepiness Scale (ESS), insomnia severity index, and Pittsburgh Sleep Quality index were significantly improved within the LT group only after the two-week period. When comparing the two groups, only the change in ESS significant was significant (mean difference; control: -0.14 vs LT: -1.43, p=0.021) after adjusting for baseline characteristics. There were no significant differences in serum cortisol or clock genes expression. Conclusions: LT can improve daytime sleepiness in patients with sleep disorders; however, further well-designed studies are warranted to confirm its efficacy.

While shoulder injuries represent the musculoskeletal disorders (MSDs) most encountered in physical therapy, there is no consensus on their management. As attempts to provide standardized and personalized treatment, a robot-ic-assisted device combined with EMG biofeedback specifically dedicated to shoulder MSDs has been developed. The aim of this study was to determine the efficacy of an 8-week rehabilitation program (≈3 sessions a week) using a ro-botic-assisted device combined with EMG biofeedback (RA-EMG group) in comparison with a conventional program (CONV group) in patients presenting with shoulder MSDs. This study is a retrospective cohort study including data from 2010 to 2013 on patients initially involved in a physical rehabilitation program in a private clinic of Chicoutimi (Canada) for shoulder MSDs. Shoul-der flexion strength and range of motion were collected before and after the rehabilitation program. Forty-four patients participated in a conventional pro-gram using dumbbell (CONV group) while 72 of them completed a program on robot-assisted device with EMG and visual biofeedback (RA-EMG group), whereby both programs consisted in 2 sets of 20 repetitions at 60% of maximal capacity. Results showed that the RA-EMG had significantly greater benefits than the Conv group for shoulder flexion strength (+103.1% vs 67%, p = 0.016) and range of motion (+14.4% vs 6.1%, p = 0.046). The current retrospective co-hort study showed that a specific and tailored rehabilitation program with constant effort by automatic adjustment of the level of resistance was able to potentiate strength and range of motion shoulder flexion after an 8-week reha-bilitation period in comparison with a conventional approach in patients with shoulder MSDs. This study provides new insight on shoulder MSD rehabilita-tion and future research should be pursued to determine the added potential of this approach for abduction and external rotation with a randomized controlled design.

Virtual reality (VR) technology gains theoretical support from rehabilitation and pedagogical theories and offers a variety of capabilities in educational and interventional contexts with affordable products. VR is attracting increasing attention in the medical and healthcare industry as it provides fully interactive three-dimensional simulations of real-world settings and social situations, which are particularly suitable for cognitive and performance training including social and interaction skills. The worldwide rising trend in the prevalence of autism spectrum disorder calls for innovative and efficacious techniques for assessment and treatment. The article offers a summary of current perspectives and evidence-based applications of VR technology as an educational and intervention tool for individuals with autism spectrum disorder, with a primary focus on social communication including social functioning, emotion recognition, and speech and language. Technology- and design-related limitations as well as the disputes over the application of virtual reality to autism research and therapy are discussed and future directions of this emerging field are highlighted with regards to application expansion and improvement, technology enhancement, and the development of brain-based research and theoretical models.

The ratio of males to females with ASD is generally quoted as 4:1 though it is believed that there are biases preventing females being diagnosed and that the true ratio is lower. These biases have not been clearly identified or quantified. Starting with a clinical dataset of 1711 children <18 years old four different methods were employed in an inductive study to identify and quantify the biases and calculate the proportion of females missed. A mathematical model was constructed to compare the findings with current published data. The true male to female ratio appears to be 3:4. Eighty per cent of females remain undiagnosed at age 18 which has serious consequences for the mental health of young women.

Purpose of the study was to explore the association of cognition with hazardous drinking, binge drinking and alcohol use disorder in schizophrenia and schizoaffective disorder. Cognitive deficits are common in schizophrenia. Alcohol might be associated with additional cognitive impairment in schizophrenia patients. The study population included 3362 schizophrenia and schizoaffective disorder patients in Finland. Hazardous drinking was screened with the AUDIT-C (Alcohol Use Disorders Identification Test for Consumption) screening tool. Binge drinking was obtained from the AUDIT-C. Alcohol use disorder (AUD) diagnoses were obtained from the national registrar data. Participants performed two computerized tasks from the Cambridge automated neuropsychological test battery (CANTAB) on tablet computer: the 5-choice serial reaction time task (5-CSRTT), or, reaction time (RT) test and the Paired Associative Learning (PAL) test. Association of alcohol use with RT test and PAL test was analyzed with log-linear regression and logistic regression, respectively. After adjustment for age, education and age at first psychotic episode, hazardous drinking in females was associated with lower median RT. Compared to never binge drinkers, male and female participants drinking 6 or more doses of alcohol monthly or less had lower median RT. In the PAL test both first trial memory score (FTMS) and total errors adjusted score (TEAS) were associated with better performance in males drinking 6 or more doses of alcohol weekly or more and in females drinking 6 or more doses monthly or less. Higher PAL TEAS was associated with AUD in females Some positive associations between alcohol and cognition were found in male and female schizophrenia and schizoaffective disorder patients with hazardous drinking and binge drinking.

Understanding the motor patterns underlying the movement of people with Parkinson's disease (PD) is fundamental to the effective targeting of non-pharmacological therapies. This study aims to analyze the gait pattern in relation to the evolutionary stage between I-II and III-IV according to the Hoehn and Yahr scale (H&amp;Y) in people affected by PD. The study was conducted with the participation of 37 PD patients, with a mean age of 70.09±9.53 years, and of whom 48.64% were women. The inclusion criteria were: 1. To be diagnosed with PD; 2. To be in an evolutionary stage of the disease of between I and IV and; 3. To be able to walk independently and without any assistance. Kinematic and spatial-temporal parameters of the gait were analyzed. The results show differences in speed of movement, cadence, stride length, support duration, swing duration, step width, walking cycle duration and double support time between the stages analyzed. These results confirm the differences in PD gait pattern between stages I-II and III-IV. Different behaviors of the same variable were recorded depending on whether the right or left sides were affected by PD.

The latest research cumulates staggering information about the correlation between the microbiota-gut-brain axis and neurodevelopmental disorders. This review aims to shed light on the potential influence of the microbiome on the development of the most prevalent neurodevelopmental disease, attention-deficit-hyperactive disorder (ADHD). As the etiology and pathophysiology of ADHD are still unclear, finding viable biomarkers and effective treatment still represent a challenge. Therefore, we focused on factors that have been associated with a higher risk of developing ADHD while simultaneously influencing the microbial composition. We reviewed the effect of a differing microbial composition on neurotransmitter concentrations important in the pathophysiology of ADHD. Additionally, we deduced factors that correlate with a high prevalence of ADHD while simultaneously affecting the gut microbiome such as emergency c-sections, and premature birth as the former leads to a decrease of the gut microbial diversity and the latter causes neuroprotective Lactobacillus levels to be reduced. Also, we assessed nutritional influences such as breastfeeding, ingestion of short-chain fatty acids (SCFAs) and polyunsaturated fatty acids (PUFAs) on the host’s microbiome and development of ADHD. Finally, we discussed the potential significance of Bifidobacterium as a biomarker for ADHD, the importance of preventing premature birth as prophylaxis and nutrition as a prospective therapeutic measurement against ADHD.

Panic disorder (PD) is characterized by recurrent and uncontrollable panic attacks associated with behavioral changes and/or persistent anxiety due to the attacks. The development of behavioral models in animals is important for the understanding of the psychobiological and behavioral bases of PD. The present article reviews the main models used in the current literature. Biobehavioral assays used in rats and mice include fear conditioning (which presents moderate predictive, face, and construct validities); the elevated T-maze (which presents good predictive validity, but low face and construct validities); electrical stimulation of the periaqueductal gray (which presents good face validity, but moderate construct validity); predator exposure models (which present good predictive and moderate construct validity); and hypercapnia-induced responses (which present moderate construct validity). These three approaches seek coherence with theories on fear as a way to increase its translational potential; thus, while the elevated T-maze is supported by the Deakin/Graeff theory, the mouse defense test battery relies on the concept of defensive distance, and periaqueductal gray stimulation is based on the functional neuroanatomy of fear. Moreover, to higher or lower degree the three models are supported by an “etho-experimental” approach, with careful observation of animal behavior as a way of discriminating different defensive strategies that model different aspects of anxiety, fear, and panic. These assays can be used, in conjunction with independent variables that attempt to simulate the vulnerabilities and stressors which lead to panic attacks, to produce true models of PD. Finally, an alternative/complementary model is proposed that uses zebrafish alarm reaction to study this disorder.

Sleep disturbances very common in children with autism. That is why it requires instruments that facilitate its evaluation. Goals: Perform the evaluation of sleep from a subjective prospect in a group of children with primary autism and compare to a control group, using the Sleep Habits in Children Survey (CSHQ), In order to determine sleep disturbances, according to the sub-scales results. Method: A prospective cross-sectional study of the sample was carried out. A group with primary Autism n = 21 was selected. For the assessment of the dream we chose (CSHQ). The differences between independent groups were calculated by applying a Mann Whitney U test (p <0.05). Results: The group of children with autism showed the highest values of the total scale (mean = 48.00) wish is congruent with a greate disfuntion of sleep, compared to the control group (mean = 36.47) for p = 0.00. Significant differences were found for all sub scales p = 0.00, with the exception of sub-scale number 7. Conclusions: There is a high presence of sleep disturbances in children with primary autism, which are related to multifactorial causes, with the exception of sleep breathing disorders that did not show statistically significant differences between groups.

Although, staging models gained momentum to stage define affective disorders, no attempts were made to construct mathematical staging models using clinical and biomarker data in patients with major depression and bipolar disorder.The aims of this study were to use clinical and biomarker data to construct statistically-derived staging models, which are associated with early lifetime traumata (ELTs), affective phenomenology and biomarkers.In the current study, 172 subjects participated, 105 with affective disorders (both bipolar and unipolar) and 67 controls. Staging scores were computed by extracting latent vectors (LVs) from clinical data including ELTs, recurring flare ups and suicidal behaviors, outcome data such as disabilities and health-related quality of life (HR-QoL), and paraoxonase (PON)1 actvities and nitro-oxidative stress biomarkers.Recurrence of episodes and suicidal behaviors could reliably be combined into a LV with adequate composite reliability (the “recurrence LV”), which was associated with female sex, the combined effects of multiple ELTs, disabilities, HR-QoL and impairments in cognitive tests. All those factors could be combined into a reliable “ELT-staging LV” which was significantly associated with nitro-oxidative stress biomarkers. A reliable LV could be extracted from serum PON1 activities, recurrent flare ups, disabilities and HR-QoL.Our ELT-staging index scores the severity of a relevant affective dimension, shared by both major depression and bipolar disorder, namely the trajectory from ELTs, a relapsing course and suicidal behaviors to progressive disabilities. Patients were classified into three stages, namely an early stage; a relapse-regression stage; and a suicidal-regression stage. Lowered lipid-associated antioxidant defenses may be a drug target to prevent the transition from the early to the later regression stages.

POH (Peri Orbital Hyperpigmentation) represents a minor clinical entity that attracts immense aesthetic damages and it generates social integration difficulties. This review focuses on the etiopathogenic causes of this entity, differentiating and reclassifying this defect as having, on the one hand, genetic causes of melanic hyperproduction – for Fitzpatrick cutaneous phototypes IV and V – and, on the other hand, both genetic and acquired vascular causes, in individuals with light-coloured skin phototypes. Hence, there is a big difference in the field of pathogenic treatment, for the two entities. In addition, this study notes the direct relationship between skin aging and POH, especially for aquired vascular causes. In this reasoning, other aesthetic deficiencies of the skin in the palpebral area should be also considered, like: blepharochalasis, wrinkles, the anatomical causes of the lower eyelid shading, symmetrical or asymmetric suborbital oedema. All of these issues will complicate the therapeutic decision and subsidiary, the pharmaceutical advice. In this context, the review shows the guidelines for a honest councelling of the patients, pointing the efficiency limit for the topical pharmaceutical medication (depigmentants, exfoliants) versus the necessity of minimally invasive or/ and surgical treatments (in blefarochalasis).

This paper introduces a novel respiratory detection system based on diaphragm wall motion tracking using an embedded ultrasound sensory system. We assess the utility and accuracy of this method in evaluating diaphragmatic function and its contribution to respiratory workload. The developed system is able to monitor the diaphragm wall activities when the sensor is placed in the zone of apposition (ZOA). This system allows the direct measurements with only one ultrasound PZT5 piezo transducer. The system both generates pulsed ultrasound waves at 2.2 MHz and amplifies reflected echoes. According to the diaphragmatic motions, the respiratory signals of the proposed system is insensitive to human motion artifacts. Promising results were obtained from six subjects on six different tests with an average sensitivity and specificity of 84% and 93% of respiration detection, respectively. Measurements are referenced to a SPR-BTA commercial spirometer. In this study, we also evaluated inertial and photoplethysmography (PPG) sensors as other conventional methods in this area.

The voluminous daily output of autism research has become increasingly disconnected, existing largely within highly specific subspecialty areas, and lacking cross-disciplinary linkages of context, theory, and findings to inform a unified body of knowledge. Robust syntheses of published research across the fields of psychiatry, cellular and molecular biology, neurology, endocrinology, immunology, behavioral and social sciences, and pedagogy may help clarify and extend current knowledge by guiding more efficient future research efforts investigating underlying causes, developmental divergences, novel treatments, and specific, sensitive biological markers in autism. This synthesis of interdisciplinary research indicates the hypothalamic-pituitary-adrenal (HPA) stress axis may be at the center of an interaction among sex steroids, immune function, signaling protein transcriptions, neurogenesis, and dysregulation of brain structures sending or receiving projections from the HPA stress axis. These interaction manifest observably as a range of sexually dimorphic behaviors and functional limitations often falling within the current diagnostic features of Autism Spectrum Disorder (ASD). The pathogenicity of endocrine dysregulation may serve as a valuable model for developing a cohesive theory of ASD by explaining how the HPA and connected brain areas respond to extreme conditions of dysregulated endocrine signaling to cause symptoms associated with autism.

Fragile X syndrome (FXS) is the most frequent monogenic form of autism spectrum disorder (ASD). Autistic FXS is caused by loss of the fmr1 gene product, the fragile X mental retardation protein (FMRP), triggering physiological and behavioral abnormalities. It is correlated with clock components for behavioral circadian rhythm. Mutation of this gene causes the disturbances in sleep patterns and circadian behavior commonly observed in patients with autistic FXS, accompanied by frequent dysregulation of melatonin synthesis and melatonin-dependent signaling. These changes impair vigilance, learning and memory, and are also linked to autistic behavior including the abnormal anxiety response. However, although several possible causes, symptoms, and clinical features of ASD have been investigated, the correlation between an altered circadian rhythm and autistic FXS has not been extensively studied. Recent works have highlighted the impact of melatonin on the nervous, immune, and metabolic systems. Even though utilization of melatonin for sleep disorder in ASD has been considered in clinical research, further studies should be aimed at its neuroprotective role in ASD during developmental period. In this review, we focus on the regulatory circuits involved in melatonin dysregulation and circadian system disruption in those with autistic FXS. Additionally, we discuss the neuroprotective effect of melatonin intervention. This may improve neuroplasticity and physical capability. We also review the underlying molecular mechanisms, and suggest that melatonin may be a useful novel treatment for autistic FXS, countering the adverse effects of circadian variation.

Starting from the fact that one of the global control strategies of the Permanent Magnet Synchronous Motor (PMSM), namely the Direct Torque Control (DTC) type control strategy, is characterised by the fact that the inner flux and torque control loop usually uses hysteresis ON-OFF type controllers and the oscillations introduced by them have to be cancelled out by the outer speed loop controller (which is usually a PI speed controller and whose performance is good around the global operating points and for relatively small variations of the external parameters and disturbances caused in particular by the load torque variation), while retaining the advantages of the DTC strategy, this paper presents the improvement of the performance of the PMSM sensorless control system using Proportional Integrator (PI), PI Equilibrium Optimizer Algorithm (EOA), Fractional Order (FO) PI, Tilt Integral Derivative (TID) and FO Lead-Lag for constant flux, and Sliding Mode Control (SMC) and FOSMC for variable flux. The performance indicators of the control system are the usual ones: response time, settling time, overshoot, steady-state error and speed ripple, plus another one given by the fractal dimension (FD) of the PMSM rotor speed signal, and the hypothesis that the FD of the controlled signal is higher when the control system provides better performance is verified. The paper also presents the basic equations of the PMSM, based on which the synthesis of integer and fractional controllers, the synthesis of an observer for estimating the PMSM rotor speed, electromagnetic torque and stator flux are presented. The comparison of the performance of the proposed control systems and the demonstration of the parametric robustness are performed by numerical simulations in Matlab/Simulink using Simscape Electrical and Fractional Order Modelling and Control (FOMCON). Real-time control based on an embedded system using a TMS320F28379D controller demonstrates the good performance of the PMSM sensorless control system based on the DTC strategy in a complete hardware-in-the-loop (HIL) implementation.

This paper presents a theory on graph topological structure and graph coloring, proving that for any N-order graph structure (with a topological structure similar to ), the maximum number of colors required for coloring is less than or equal to . The Four Color Theorem is just one special case of this theory, with the maximum structure size for a four-color graph being a 4-order structure graph, hence requiring only a maximum of 4 colors for coloring.

This research focuses on the Hopf bifurcation occurring at a singular point with high-order degeneracy in a class of three-dimensional systems. Based on center manifold theorem, by expanding the technique for determining singular point values of degenerate singularities in two-dimensional systems, the formal series method to determine the singular point values at the high-order degenerate critical point is discussed. Furthermore, a class of specific three-dimensional differential systems with high-order degenerate singularities is explored. By computing the singular point values and determining center conditions, we proved that a minimum of 5 small-amplitude limit cycles in the vicinity of the origin. This research provides new perspectives on the center problem and limit cycle bifurcation in high-dimensional systems with high-order degenerate singular points.

Chipman contends, in stark contrast to the conventional view, that utility is not a real number but a vector and is inherently lexicographic in nature. According to these views, it will be proved that, for every preordered topological space (X,≾,t), the assumption t to be finer than the order topology t≾ on (X,t), i.e. t⊇t≾, and the assumption the quotient topology t∣∼≾ that is defined on the preordered set (X∣∼,≾∣∼) of indifference classes of ≾ to be Hausdorff imply that a cardinal number κ and a (complete) preorder ≲ on {0,1}κ that is coarser than the lexicographical ordering ≤lex on {0,1}κ, i.e. ≤lex⊂≲, can be chosen in such a way that there exists a continuous order-embedding ϑ:(X,≾,t)⟶({0,1}κ,≲,t≤lex). This theorem will be compared with a theorem that, in particular, describes necessary and sufficient conditions for ≾ to have a continuous multi-utility representation.

This study presents finite-time synchronization of chaotic fractional order systems with disturbance uncertainty and unknown time delay. First, a PID sliding surface is presented. Then, for the finite-time synchronization of the master and slave systems, a robust sliding-adaptive control method is provided. Update rules have been retrieved to estimate the system parameters by using the Lyapunov function while establishing the stability of the suggested mechanism and assuring the convergence of errors to zero. The proposed approach was used to solve the fractional order system of Gensou-Tsei with time-varying parameters, and the simulation results show that the presented approach performs well.

We investigate numerically the interactions of in-phase Airy beams modulated by a fundamental Gaussian beam and fourth-order diffraction in Kerr nonlinear media. Directly numerical simulations show that normal (anomalous) fourth-order diffractions and in-phase (out-of-phase) Gaussian beam affect the interactions of solitons generated from Airy beams in unique manners. Different from previous results that interactions between in-phase (out-of-phase) conventional beams are always attractive (repulsive), many anomalous interactions of Airy beams are obtained. Stable bound states of breathing Airy soliton pairs can be formed with the help of fourth-order diffraction and fundamental Gaussian beam.

As a manifestation of technological innovation achievements, patents reflect the frontier of technological development in the field. This paper constructs a multi-entity patent collaboration network and analyzes its evolution patterns to provide a reference for innovation organizations to grasp the general direction of innovation development and select partners. Based on the data of collaboration patents of two and three organizations in China's new energy vehicles (NEV) industry from 2008 to 2021, this paper constructs higher-order interaction collaboration innovation networks in different periods to analyze the spatial evolution of networks and patent cooperation activities. The results show that the generalized degree distribution of nodes and edges conforms to the power-law distribution. The two-dimensional simplex networks gradually form triangle groups centered on the core nodes, and the importance of key edges is beginning to emerge. Key organizations and partners, including State Grid Corporation of China, Tsinghua University, and China Electric Power Research Institute, will largely determine the direction for the future development of cooperation innovation. The study also reveals that Beijing, Jiangsu, Zhejiang, Guangzhou, and Shanghai are in central positions in the cooperative innovation network, based on their cooperation breadth and depth measures. Additionally, key links formed by Beijing as hubs are not only the center of information exchange but also function as bridges that connect to other regions.

The gradient descent algorithm has become the standard algorithm for computing the extreme values of functions, but for multivariate functions this algorithm is mostly ineffective. This is because the convergence rate of each element is inconsistent in most cases. However, in this paper, we found that the gradient sign is a self-optimizing operator, ensuring that the convergence rate is consistent across all elements. This also explains, from an optimization perspective, the success of the Fast Gradient Sign Method (FGSM) in generating adversarial samples that are indistinguishable from the normal input, but can easily fool neural networks. We also found that the fractional order gradient is also self-optimizing, and that the convergence speed of this algorithm can be controlled by adjusting the order of the gradient. Experiments suggest that this algorithm not only generates adversarial samples faster than other algorithms, but that a single source image can generate many such samples. This algorithm is also more effective than others at generating adversarial samples from simple images.

We characterize the form of the order divisor graph of cyclic groups based on the group order. Furthermore, we describe some topological indices of the order divisor graph of cyclic groups, including the Wiener index, Harary index, first Zagreb index, and second Zagreb index.

A stochastic dominance (SD) relation can be defined by two different perspectives: One from the view of distributions, and the other one from the view of expected utilities. In early days, Fishburn investigated SD from the view of distributions and we refer this perspective as Fishburn’s SD. One of his many results was the development of fractional-order SD for continuous distributions. However, discrete fractional-order SD may not be generalized directly since some properties of fractional calculus do not have a discrete counterpart. In this paper, we develop a discrete analogue of fractional-order SD from the view of distributions. We generalize the order of SD by Lizama’s fractional delta operator, show the preservation of SD hierarchy, and formulate the utility classes that are congruent with our SD relations. This work brings a message that some results of discrete SD cannot be generalized directly from continuous SD. We characterize the difference between discrete and continuous fractional-order SD, as well as the way to handle them.

Time series data having low signal-to-noise ratio, non-stationarity and non-linearity are commonly seen in high-frequency stock trading, where the objective is to increase the likelihood of profit by taking advantage of tiny discrepancies in prices and trading on them quickly and in huge quantities. For this purpose, it is essential to apply a trading method that is capable of fast and accurate prediction from such time series data. In this paper, we develop an online time series forecasting method for high-frequency trading (HFT) by integrating three neural network deep learning models, i.e., Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU) and Transformer; and we abbreviate the new method to online LGT or O-LGT. The key innovation underlying our method is its efficient storage management, which enables super-fast computing. Specifically, when computing the forecast for the immediate future time, we use only the output calculated from the previous trading data (rather than the previous trading data themselves) together with the current trading data. Thus, the computing involves updating only the current data into the process. We evaluate the performance of O-LGT by analyzing the high-frequency Limit Order Book (LOB) data from the China market. It shows that our model in most cases achieves similar speed with much higher accuracy than the conventional fast supervised learning models for HFT. However, with a slight sacrifice in accuracy, O-LGT is approximately 40 times faster than the existing high-accuracy neural network models for the LOB data in China market.

Transcription factors (TFs) are proteins that recognize specific DNA fragments in order to decode the genome and ensure its optimal functioning. TFs work at the local and global scales by specifying cell type, cell growth and death, cell migration, organization and timely tasks. We investigate the structure of DNA-binding motifs with the theory of finitely generated groups. The DNA ‘word’ in the binding domain -the motif- may be seen as the generator of a finitely generated group Fdna on four letters, the bases A, T, G and C. It is shown that, most of the time, the DNA-binding motifs have subgroup structure close to free groups of rank three or less, a property that we call ‘syntactical freedom’. Such a property is associated to the aperiodicity of the motif when it is seen as a substitution sequence. Examples are provided for the major families of TFs such as leucine zipper factors, zinc finger factors, homeo-domain factors, etc. We also discuss the exceptions to the existence of such a DNA syntactical rule and their functional role. This includes the TATA box in the promoter region of some genes, the single nucleotide markers (SNP) and the motifs of some genes of ubiquitous role in transcription and regulation.

We study the validity of quantum Fisher information (QFI) as a faithful quantum coherence and correlation quantier by drawing a comparison with subsystem's coherence measure, rst-order coherence (FOC) and the entanglement measure, Negativity to study the behavior of thermal quantum coherence and correlations in two qubit Heisenberg XXX model, placed in independently controllable magnetic eld by systematically varying the coupling parameter, magnetic eld and bath temperature for ferromagnetic and antiferromagnetic case. After carefully observing the prole of quantum coherence and correlation measures, we propose an inequality relations which shows that there may exist a quantitative relationship between QFI, Negativity and FOC in which, the equality exists at zero temperature. We identify QFI to be a more useful coherence quantier, as it quanties coherence of individual subsystems and correlations among the subsystems. On the other hand, FOC identies coherence present in the individual subsystems only. A reciprocal relationship between Negativity and FOC is also observed in dierent cases. We also observe the existence of entanglement in ferromagnetic case, in contrast to simple Heisenberg XXX model in uniform magnetic eld. We show that in the ferromagnetic case, a very small inhomogeneity in magnetic eld is capable of producing large values of thermal entanglement. This shows that the behavior of entanglement in the ferromagnetic Heisenberg system is highly unstable against inhomogeneity of magnetic elds, which is inevitably present in any solid state realization of qubits.

Anyon is collective excitation of two dimensional electron gas subjected to strong magnetic field, carrying fractional charges and exotic statistical character beyond fermion and boson. So far, anyons with serial fractional charges only exist in fractional quantum Hall effect. It is still a challenge to find new serial of fractional charges in other physical system and develop an unified mathematical physics theory based on the same root. Here a topological path fusion theory of propagating electrons in magnetic flux lattice is proposed to explore the physical origin of fractional charges based on a generalization of Feynman's path integral theory and Thurston's train track theory. This mathematical physics theory generated the existed serial of fractional charges in fractional quantum Hall effect and predicted new serial of fractional charges. A serial of irrational charges are predicted in one dimensional lattice of magnetic fluxes. Fractionally charged anyons are also generated in two dimensional and three dimensional lattice of train tracks of electric currents, revealing an exact correspondence between knot lattice model and train track model. A new explanation for the modular symmetry of complex Hall conductance and composite fermion in fractional quantum Hall effect is also derived from this topological path fusion theory. Experimental observation of anyon in three dimensions can be realized by constructing three dimensional interlocking magnetic fluxes or mapping magnetic fluxes into forbidden zones in multi-connected space filled by solid state material. A photonic crystal with porous nano-structures is a promising system for detecting fractional charges and paves a new way for topological quantum computation.

Brazil, Russia, China, India, and the Republic of South Africa (BRICS) represent developing economies facing different energy and economic development challenges. The current study aims to forecast energy consumption in BRICS at aggregate and disaggregate levels using the annual time series data set from 1992 to 2019 and to compare results obtained from a set of models. The time-series data are from the British Petroleum (BP-2019) Statistical Review of World Energy. The forecasting methodology bases on a novel Fractional-order Grey Model (FGM) with different order parameters. This study contributes to the literature by comparing the forecasting accuracy and the forecasting ability of the FGM(1,1) with traditional ones, like standard GM(1,1) and ARIMA(1,1,1) models. Also, it illustrates the view of BRICS's nexus of energy consumption at aggregate and disaggregates levels using the latest available data set, which will provide a reliable and broader perspective. The Diebold-Mariano test results confirmed the equal predictive ability of FGM(1,1) for a specific range of order parameters and the ARIMA(1,1,1) model and the usefulness of both approaches for energy consumption efficient forecasting.

This paper presents Chebyshev Type inequalities for the Riemann-Liouville variable-order fractional integral operator using two synchronous functions on the set of real numbers. It is the first result of its kind in the current literature using variable-order Riemann-Liouville fractional integral operator. Some special cases for the result obtained in the paper are discussed.

Despite the successes earned in cataloguing and finding the role of the most of molecular components in living matter, the “biochemical and molecular” perspective, popular in biology, medicine and genetics, is unable to give account for crucial topics as the faculty of living systems to “feel”, to “perceive” what a given stimulus implies (means, indeed) for their survival. Condensed matter physics too, if bounded to a local, short-range, and perturbative approach, fails dramatically. This is also due to the role commonly assigned to water – actually the main constituent of living matter – deemed for long time to be merely chemical (as “solvent” or a reactant/product). Nonetheless, today many evidences show how living matter can be right conceived as a super-structured coherent water-based matrix, suggesting that the characterization of bio(electro)chemical and physical processes undertaken at molecular level in living matter, would let us unable to answer a question like this: what allows an amoeba, moreover without any neurons, to “know” to get closer to a nutrient or escape away from a toxin? I propose that to pursue such a fundamental inquiry it’s necessary an essentially relational approach, that is: to consider the living being at its grounding as the outcome of a physical history of relationships where symmetry-breakings, dissipation and coherence yield the emergence of the living state of matter, conceivable only as a time-dependent open process, and not as a portraited “body”. The effective tools to build up such an approach may be retrieved in far-from-equilibrium thermodynamics (TD), symmetry-breakings and gauge-fields theory, science of complexity, within the framework of a Quantum Field Theory. Indeed, within a field-view of matter, and of water especially, as it has been developed by a Quantum Electrodynamic (QED) description of condensed matter, it’s possible to give account for a physical basis too such an epistemologically elusive, though crucial, feature of living systems (i.e.: perception and meaning). The emerging landscape allows some important meditations about adaptation, evolution, ecodynamics, and about different conceptions of complexity and “information” in living realm. Furthermore, some neuroscientific themes like consciousness, qualia and their links to artificial intelligence could be supplied with due insights.

We search for limit cycles in the dynamical model of two-species chemical reactions that contain seven reaction rate coefficients as parameters and at least one third-order reaction step, that is, the induced kinetic differential equation of the reaction is a planar cubic differential system. Symbolic calculations were carried out using the Mathematica (or, Wolfram language) mathematical program package, and it was also used for the numerical verifications to show the following facts: the kinetic differential equations of these reactions each has two limit cycles surrounding the stationary point of focus type in the positive quadrant. In the case of Model 1, the outer limit cycle is stable and the inner one is unstable that appears in a supercritical Hopf bifurcation. Moreover, the oscillations in a neighborhood of the outer limit cycle are slow-fast oscillations. In the case of Model 2, the outer limit cycle is unstable and the inner one is stable. With another set of parameters, the outer limit cycle can be made stable and the inner one unstable.

Change orders have received considerable attention from researchers thus far, but none have considered pricing strategies of change orders through the interaction between general contractors and subcontractors. Previous studies found that contractors’ opportunistic bidding considering beyond-contractual reward (BCR) in the execution stage can be reduced by improving the construction management system and strengthening the supervision of contractors’ performance. However, the BCR remains in ecology of construction engineering. This study proposes an integrated evolutionary game theory-system dynamics model (ET-SD model) and simulates the pricing strategy of change orders between general contractors and subcontractors to explore the root cause of BCR phenomenon. Sensitivity analysis on the evolutionary dynamics of payoff is explored. Results reveal that change orders with BCR maintain Nash equilibrium and evolutionary stable strategy (ESS) unless changing the payoff structure between general and subcontractors’ pricing strategies. This study presents important managerial insights from the evolutionary game perspectives, nature of change orders, and payoff of the alternative.

Permanent magnet synchronous motor (PMSM) AC servo system shows the characteristics of uncertainty, time-varying and non-linearity, which makes it difficult for traditional PID control to achieve ideal control effect. Fuzzy control has strong adaptability to the problems of parameter variation, non-linearity and model inaccuracy of the controlled object. Vector control strategy is used to study its control principle and the realization of SVPWM. Because the motor object has certain fractional order characteristics, the fuzzy parameter self-tuning PIλ control is chosen as the position regulator of servo motor. It combines the accuracy of fractional order PI control and the adaptability of fuzzy control. A simulation model of PMSM three-closed-loop system is built in MATLAB/Simulink environment. The results show that the control method is effective and can satisfy the trajectory tracking of servo control.

A new approach is presented to defining the amount of information, in which information is understood as the data about a finite set as a whole, whereas the average length of an integrative code of elements serves as a measure of information. We obtain a formula for the syntropy of reflection, that is, the information that two intersecting finite sets reflect about each other. The specificities of reflection of discrete systems through the combination of their parts are considered, and it is shown that the additive syntropy and entropy of reflection are measures of structural order and chaos. Three information laws have been established: the law of conservation of the sum of chaos and order; information law of reflection; the law of conservation and transformation of information. An assessment of the structural organization and the level of development of discrete systems is presented. It is shown that various measures of information are structural characteristics of integrative codes of elements of discrete systems. The conclusion is made that, from the informational-genetic positions, the synergetic theory of information is primary in relation to the Hartley-Shannon information theory. In the appendix we consider the asymmetry of the mutual reflection of finite sets and the proportionality of arbitrary quantities.

The origination of most free-living animal species is predictable. ‘Light’ order production in the trophic levels below is the key. Absent from the abiotic environment (pre-primary level) and prokaryotes, ‘light’ order consists of differences in species composition between highly-developed, species-packed communities in which extinction may be the precursor to speciation, particularly ecologically comparable tropical continental lowland ones of Africa, Australia, India, and South America. Based on but not itself of matter and non-burnable, its nil waste heat content ‘compensates’ for burned-up food energy. Where the amounts of it on infra-apical trophic levels form an inverted pyramid like waste heat’s, occupancy of trophic levels from primary to apical is predictable (variation and selection assumed). Terminal non-inversion predicts post-apical vacancy. Examined communities were from grasslands (grasses, large grazing mammals, large carnivores), and woodlands (woody plants, butterflies, insect-eating birds, raptorial birds). Linnaeus’s binomial classificational system turned out to be unexpectedly rich in new content. A new object of study, the neoLinnaean, is recognised and a new process, the neoLyellian, advanced to explain it. Relations to Darwin’s and Lyell’s non-predictive theories of change, to ecology and evolution, to thermodynamics, statistical mechanics and quantum mechanics, and to neurology are discussed.

This study presents an exact solution to the free vibration analysis of a uniform Timoshenko beam, with a harmonic vibration being assumed. The Timoshenko beam theory covers cases associated with small deflections based on shear deformation and rotary inertia considerations. In this paper, a moment-shear force-circular frequency-curvature relationship was presented. The complete study was based on this relationship and closed-form expressions of efforts and deformations were derived. The free vibration response of single-span systems and spring-mass systems was analyzed; closed-form formulations of matrices expressing the boundary conditions were presented and the natural frequencies were determined by solving the eigenvalue problem. Systems with intermediate mass, spring, or spring-mass system were also analyzed. Furthermore, first-order dynamic stiffness matrices in local coordinates were derived. Finally, a second-order analysis of beams resting on an elastic Winkler foundation was conducted. The results obtained in this paper were in good agreement with those of other studies.

Neurodegeneration is a major problem in neuroscience and medicine as it causes and worsens many neurological and psychiatric disorders. It involves gradual loss of neurons at different levels of the brain, affecting cognition, emotion, behavior, consciousness, and attention. Understanding the causes and consequences of neurodegeneration is crucial for identifying the risk factors, biomarkers, and treatments. However, the current therapies are mostly symptomatic and ineffective. Therefore, new and innovative methods, such as noninvasive brain stimulation (NIBS), are needed to modulate brain activity and plasticity in a safe and reversible manner. This field is rapidly developing, with a focus on exploring new clinical applications, mechanisms, and combinations of NIBS and drugs. This special issue covers the latest progress and challenges in the study of neurodegeneration in cognitive impairment and mood disorders from the experimental, clinical, and translational perspectives. It addresses the complexity and diversity of these conditions, the lack of disease-modifying treatments, the ethical and practical issues of conducting research in vulnerable and diverse populations, the integration and interpretation of data from multiple sources and modalities, and the development and validation of novel methods such as NIBS, artificial intelligence, and drug repurposing. Collaborative efforts among various stakeholders are needed to improve our knowledge and treatment of neurodegeneration underlying cognitive impairment and mood disorders.

Investigation of functional magnetic resonance imaging (fMRI) data with machine learning (ML) techniques, including also deep learning (DL) methods, have been widely used to study Autism Spectrum Disorder (ASD). This disorder is characterized by symptoms that affect the individual’s behavioral aspects and social relationships. Early diagnosis is crucial for intervention, but the complexity of ASD poses challenges for treatment development. This study compares traditional ML techniques with deep learning (DL) methods in the analysis of functional connectivity measures obtained from the time series of multicentric ABIDE dataset. Specifically, Support Vector Machines (SVM) classifiers, with both linear and Radial Basis Function (RBF) kernels, as well as eXtreme Gradient Boosting (XGBoost) classifiers, are compared against the TabNet classifier, which is a DL architecture customized for tabular data analysis and a Multi Layer Perceptron (MLP). The findings suggest that DL classifiers may not be optimal for the type of data analyzed, as their performance trails behind that of standard classifiers. SVMs achieve performances, in terms of AUC, around 75%, compared to the best TabNet and MLP results, which are 65% and 71%, respectively. Additionally, this work investigates the brain regions that contribute most to the classification task, which are found to be those primarily responsible for sensory and spatial perception, as well as attention modulation, known to be altered in ASD.

High entropy alloys have excellent wear resistance, so they have great application prospects in the fields of wear resistance and surface protection. In this study, the wear resistance of FeNiCrCoCu high entropy alloy coating was systematically analyzed by molecular dynamics method. FeNiCrCoCu high entropy alloy was used as a coating material to adhere to the surface of Cu matrix. The friction and nano indentation simulation of this coating material were carried out by controlling the ambient temperature. The influence of temperature on its friction properties was analyzed from five aspects: lattice structure, dislocation evolution, friction coefficient, hardness and elastic modulus. The results show that with the increase of temperature, the disorder of the lattice structure increases, which leads to the increase of the tangential force and the friction coefficient in the friction process. At 300K and 600K, the ordered lattice structure of the high entropy alloy coating material is basically the same, and thus their hardness is basically the same. However, the dislocation density at 600K is significantly reduced compared with that at 300K, resulting in the increase of the elastic modulus of the material from 173GPa to 219GPa. At the temperatures of 900K and 1200k, lattice disorder take place rapidly, and dislocation density also decreases significantly, resulting in a significant decrease in the hardness and elastic modulus of the material. When the temperature reaches 900K, the wear resistance of the FeNiCrCoCu high entropy alloy coating decreases sharply. This work is of great value to the analysis of wear resistance of high entropy alloy at high temperature.

Autism spectrum disorder (ASD) is a neurodevelopmental disorder characterized by impairments in social interaction and communication, anxiety, hyperactivity, and restricted interest for specific subjects. In addition to the genetic factors, multiple environmental factors have been related to the development of ASD. Animal models can serve as crucial tools for understanding the complexity of ASD and identifying potential therapeutic strategies. In this study, a chemical model of ASD has been developed in zebrafish by exposing embryos to valproic acid (VPA) from 4 to 48 hours post-fertilization. After the exposure, embryos were transferred to fish water and the effects on behavior and neurotransmitters profile were analyzed at both larval and adult stages. Larvae from VPA-treated embryos showed hyperactivity and decreased visual and vibrational escape responses, as well as an altered neurotransmitters profile, with increased glutamate and decreased acetylcholine and norepinephrine levels. Adults from VPA-treated embryos exhibited impaired social behavior characterized by larger shoal sizes and a decreased interest for their conspecifics. Neurotransmitter analysis revealed a significant decrease in dopamine and GABA levels in the brain. These results support the interest of this model in the study of ASD.

This article aims to determine the different factors that are associated with anxiety symptoms among Filipino farmers in Central Luzon. Eligible participants were screened for anxiety symptoms using the Generalized Anxiety Disorder-7 (GAD-7), while the Data Collection Tool for Factors Associated with Anxiety Symptoms (DCFAAS) was constructed and validated to determine the exposure of these factors. Among the 113 eligible farmers, only 19 (16.8%) experienced anxiety symptoms, with excessive worrying (39.8%) being the most frequent anxiety symptom. The mental health of Filipino farmers was significantly affected by the presence of physical illness (OR = 10.70 [1.367, 83.773]) and having relatives affecting work completion (OR = 6.45 [1.346, 30.896]). Therefore, policies must be focused on tackling these issues to address the mental health issues faced by farmers. The low prevalence of anxiety symptoms may stem from existing perceptions and barriers encountered in confiding psychological issues, and the Filipino’s religiosity and resiliency. Keywords: Farmers, Agriculture, Central Luzon, Generalized Anxiety Disorder, GAD-7

Anxiety disorders affect patient quality of life (QoL). This study aimed to examine the associations between improved QoL and reduced clinical symptoms and Korean medicine (KM) in patients with anxiety disorders. Medical records of patients with anxiety who were treated with KM (acupuncture, Anxiety disorders affect patient quality of life (QoL). This study aimed to examine the associations between improved QoL and reduced clinical symptoms and Korean medicine (KM) in patients with anxiety disorders. Medical records of patients with anxiety who were treated with KM (ac-upuncture, psychotherapy, Chuna therapy, aromatherapy, or herbal medicine) for at least 4 weeks were retrospectively analyzed. Clinical, QoL, and cost outcomes were measured at baseline and at 4 and 12 weeks (Anxiety: State-Trait Anxiety Inventory[STAI X-1(state), X-2(trait)], Beck Anxiety Inventory[BAI]; anger: State-Trait Anger Expression Inventory State[STAXI-S(state), T(trait)], Anger Expression Inventory[AXI-K-I(anger-in), AXI-K-O(anger-out), AXI-K-C(anger-control); depression: Beck Depression Inventory-II[BDI II], QoL: QoL-related instruments Euro Quality of Life 5 Dimensions utility score[EQ-5D], Euro QoL Visual Analog Scale[EQ-VAS]). The quali-ty-adjusted life years (QALYs) and average cost-effectiveness ratio of KM for anxiety were esti-mated. The medical records for 67 patients were evaluated. KM treatments were found to be as-sociated with decreased anxiety (STAI X-1; STAI X-2; BAI, p

Cancer-associated cachexia (CAC) is a debilitating condition marked by muscle and fat loss, unresponsive to nutritional support, and contributes significantly to morbidity and mortality in patients with cancer. Immune dysfunction, driven by cytokine imbalance, contributes to CAC progression. This review explores the potential relationship between CAC and anti-cancer immune response in pre-clinical and clinical studies. Pre-clinical studies showcase the involvement of cytokines like IL-1β, IL-6, IL-8, IFN-γ, TNF-α, and TGF-β, in CAC. IL-6 and TNF-α, interacting with muscle and adipose tissues, induce wasting through JAK/STAT and NF-κB pathways. Myeloid-derived suppressor cells (MDSCs) exacerbate CAC by promoting inflammation. Clinical studies confirm elevated pro-inflammatory cytokines (IL-6, IL-8, TNFα) and immune markers like neutrophil-to-lymphocyte ratio (NLR) in patients with CAC. Thus, immunomodulatory mechanisms involved in CAC may impact the anti-neoplastic immune response. Inhibiting CAC mechanisms could enhance anti-cancer therapies, notably immunotherapy. R-ketorolac, a new immunomodulator, reversed weight loss and increased survival in mice. Combining these agents with immunotherapy may benefit patients with cancer experiencing CAC. Further research is vital to understand the complex interplay between tumor-induced immune dysregulation and CAC during immunotherapy.

Despite the clinical significance of partial posttraumatic stress disorder (PTSD), research on its associated cognitive function profiles is notably lacking. This study aimed to identify distinct cognitive function profiles in patients with partial PTSD. Medical records of Vietnamese War Korean Veterans undergoing psychiatric assessments, including the Clinician-Administered PTSD Scale (CAPS) and neurocognitive tests on the same day, were retrospectively reviewed. The participants were divided into partial PTSD (N = 10) and non-PTSD (N = 36) groups based on the CAPS interview. Neurocognitive test score profiles of the partial PTSD and non-PTSD groups were compared. Covariance analysis adjusted for potential confounders in cognitive function. In cognitive profiles, executive function, mental flexibility (measured by Trail Making Test B), and concentration (measured by the Mini-Mental Status Exam) were significantly poorer in the partial PTSD group. When adjusted for age, depressive symptoms, and education level, no significant differences were observed in the cognitive profiles between the partial PTSD and non-PTSD groups. In the current study, executive function and concentration showed deficits in patients with partial PTSD compared with those without PTSD. Partial PTSD cognitive profiles displayed no significant differences from non-PTSD profiles when adjusted for possible confounders such as depressive symptoms.

Introduction: Substance-induced psychotic disorder (SIPD) is a mental health condition linked to the use of a psychotropic substance whose pathogenesis has not been totally clarified. The present retrospective observational study has the objective to identify differences in clinical features and peripheral biomarkers in SIPD patients according to the main substance of abuse.Methods: A total sample of 218 patients was divided in 3 groups according to the type of consumed substance: alcohol, cannabis and psychostimulants. The 3 groups were compared for continuous variables by one-way analyses of variance (ANOVAs) and for qualitative variables by χ² tests. After removing alcohol-induced psychotic disorder group, the same analyses were repeated. Statistically significant variables from these latter analyses were included in a binary logistic regression model to confirm their reliability as predictors for cannabis- or psychostimulants-induced psychotic disorders. Results: Psychotic cannabis abusers were younger (p<0.01) and had an earlier age at onset (p<0.01). On the contrary, alcohol consumers resulted to have a longer duration of illness (p<0.01), more previous hospitalizations (p=0.04), more frequent medical comorbidity (p<0.01), higher mean Modified Sad Person Scale scores (p<0.01). Finally, psychostimulant abusers had more frequently a lifetime history of poly-substance use disorders (p<0.01). Binary logistic regression revealed that higher mean Brief Psychiatric Rating Scale scores (p<0.01) higher sodium (p=0.012) and haemoglobin(p=0.040) plasma levels were predictors of cannabis misuse in SIPD patients.Conclusions: Different clinical factors and biochemical parameters con be associated to SIPD according to the main substance of abuse, thus requiring a specific management by clinicians.

Increasing research has indicated a strong association between identity functioning and eating disorder (ED) symptomatology. However, a detailed investigation of identity throughout ED treatment is lacking. The present longitudinal study examined identity in inpatients with an ED and explored its co-development with ED symptomatology throughout treatment. At admission (Wave 1), 110 women with an ED (Mage=21, SD=6.08, range:14-45) participated, of whom 83.64% participated during treatment (Wave 2) and 55.45% at discharge (Wave 3). Questionnaires on identity synthesis, identity confusion, identity processes, and ED symptomatology were completed. Latent growth curve modeling was used to address the research questions. Throughout treatment, a decrease in identity confusion and an increase in identity synthesis and adaptive identity processes were found. Accordingly, increases in identity synthesis and identification with commitment were related to general decreases in drive for thinness and body dis-satisfaction. Similarly, such decreases in ED symptoms were related to general decreases in identity confusion and ruminative exploration. The present study points to an increase in identity functioning throughout treatment, and longitudinal associations between identity functioning and ED symptomatology were found. Helping the patient to decrease ruminative exploration and to increase identification with previously made life commitments seem important in ED treatment.

Bipolar disorder (BD) is a high suicide-risk mental disorder. The purpose of this study was to identify the relationship between temperament and character traits with suicide probability, suicide attempt and perceived stress level in patients with BD. A total of 39 euthymic patients with bipolar disorder who had a history of suicide attempt and 39 euthymic patients without a history of suicide attempt were included in this study. Sociodemographic and clinical data form, Hamilton Depression Rating Scale (HDRS), Young Mania Rating Scale (YMRS), The Structured Clinical Interview for DSM-5-Clinician Version (SCID-5/CV), Temperament and Character Inventory (TCI), Perceived Stress Scale (PSS), Suicide Probability Scale were used for obtaining the data. HDRS, PSS and SPS scores of the group comprised of patients who attempted suicide were higher than the other group. There was no significant difference between the group of patients who had attempted suicide and the other group in terms of temperament characteristics. In the group of patients who had attempted suicide, self-directedness (SD) and cooperativeness (CO) scores were lower and self-transcendence (ST) score was higher than the other group. HA and ST were positively and SD negatively associated with SPS scores. In the regression analysis for suicide risk, the factors most associated with suicide risk were high HDRS and low CO score. Low SD in BD, high ST with CO may be associated with suicide attempt. Alongside low SD, high HA and ST may be associated with suicidal ideation. Treating residual depressive symptoms can reduce the risk of suicide.

In vertebrates, oligodendrocytes (OLs) are glial cells of the central nervous system (CNS) responsible for the formation of the myelin sheath that surrounds the axons of neurons. The myelin sheath plays a crucial role in the transmission of neuronal information by promoting the rapid saltatory conduction of action potentials and providing neurons with structural and metabolic support. Saltatory conduction, first described in the peripheral nervous system (PNS), is now generally recognized as a universal evolutionary innovation to respond quickly to the environment: myelin helps us think and act fast. However, the function of myelin in the CNS, particularly in the brain, is not necessarily to act quickly, but rather to act correctly. In this respect, myelin should primarily play a role in synchronizing the different neuronal networks, a synchrony that occurs in the form of oscillations (or rhythms) relevant for specific information processing. Interestingly, myelin has been directly involved in different types of cognitive processes relying on brain oscillations, and myelin plasticity is currently considered to be part of the fundamental mechanisms for memory formation and maintenance. However, despite ample evidence showing the involvement of myelin in cognition and neurodevelopmental disorders characterized by cognitive impairments, the link between myelin, brain oscillations, cognition and disease is not yet fully understood. In this review, we aim to highlight what is known and what remains to be explored to understand the role of myelin in high order brain processes.

Constant exposure to a variety of environmental factors has become increasingly problematic. A variety of illnesses are initiated or aided by the presence of certain perturbing factors. In the case of autism spectrum disorder, the environmental component begins to play an important part in de-termining the overall picture. Moreover, the lack of therapies to relieve existing symptoms com-plicates the fight against this condition. As a result, the use of animal models has made biomedical research easier and more suited for disease investigations. The current study used zebrafish as an animal model to mimic a real-life scenario: acute exposure to an increased dose of pesticides, fol-lowed by prospective intervention-based therapy with vitamin B12 (vit. B12). It is known that vit. B12 is involved in brain function, nerve tissue and red blood cell formation. Aside from this, the role of vit. B12 in the redox processes was recognized for its help against free radicals. To investigate the vit. B12 effect, fish were divided in four different groups and exposed to a pesticide mixture (600 μg L-1 fipronil + 600 μg L-1 pyriproxyfen) and 0.24 μg L-1 vit. B12 for 14 days. The impact of the compound was assessed daily by EthoVision software for behavioral observations, especially for sociability, quantified by the social interaction test. In addition, at the end of the study, the activities of su-peroxide dismutase (SOD), glutathione peroxidase (GPx), and malondialdehyde (MDA) were measured. Results showed significant improvements for locomotor activity parameters and a pos-itive influence of vitamin on sociability. Regarding the state of oxidative stress, a high activity was found for SOD and GPx in the case of vit. B12, while the mixture of pesticides and vitamin had a lower level of MDA. In conclusion, the study brings new data about the effect of vit. B12 in zebrafish, highlighting the potential use of vitamin supplementation to maintain and help the organism function.

The study aimed to determine, through a systematic review, the relationship between Reactive Attachment Disorder (RAD) and the presence of psychopathology in children and adolescents and to determine the existence of differences about the presence of internalising and externalising psychological problems between the RAD group and groups with other disorders or with typical development. Following the PRISMA methodology, a search was carried out in the Web of Science, PubMed and Scopus databases. The search yielded 770 results, of which only 25 met the inclusion criteria. The results, indicate a relationship between the presence of RAD and/or Disinhibited social engagement disorder (DSED) with the presence of internalising and externalising problems. These difficulties are more present in children with RAD compared to children without personal difficulties, or children with DSED, children with autism, children with intellectual disabilities or children with hyperactivity. It can be concluded that the presence of RAD has negative consequences on the mental health of children and adolescents, with these being greater in the inhibited group than in the disinhibited group, and with respect to children with autism or hyperactivity.

IIt is important that Master’s Degree students in psychology, regardless of their future specialisa-tion, know about Attention Deficit Hyperactivity Disorder (ADHD). The aim is evaluate and an-alyse the ADHD-related knowledge held by students of Early Childhood Education and Primary Education. The findings that were obtained in this study (on the chronicity of the disorder, changes in manifestations throughout the life cycle, and the importance of early and multidisci-plinary intervention) have clear implications in relation to the specific training in ADHD for fu-ture teachers. It is essential to broaden the specific training in relation to ADHD in studies for the Master’s Degrees in Early Childhood Education and Primary Education and through contin-uous training.

Functional neurological disorders (FND) are relatively frequent disabling conditions. Given their complexity, multidisciplinary rehabilitation is envisaged. Whole-body cryostimulation (WBC) represents a promising adjuvant treatment, mainly because of its anti-inflammatory and exercise-mimicking effects. Here, we propose a novel use of WBC in a wheelchair-ridden 61-year-old woman diagnosed with FND and other comorbidities, who, after several therapeutic and rehabilitation failures, underwent a multidisciplinary rehabilitation program including WBC. The 4-week program included physiotherapy, nutritional intervention, psychological support, and WBC. Questionnaires to assess disease impact, pain level, perceived fatigue and sleep quality were administered. At discharge, improvements in body composition, haematological biomarkers, physical performance, and questionnaire scores were observed. The patient was able to walk with a walker for medium distances, even outdoors. We observed unprecedented improvements, particularly in functional parameters and questionnaire scores. Although we cannot ascertain to which extent WBC per se contributed to the improvements measured, subjective reporting and our clinical observation were that WBC, the only intervention not previously experienced by the patient, acted as a booster for the rehabilitation interventions. Further research will be necessary to rule out any possible placebo effect and to assess the effects of WBC on FND.

Autism Spectrum Disorder (ASD) is a neuro developmental condition that impacts social interac-tion, communication, and behavior. It is called a spectrum because its symptoms and degrees of severity can vary greatly from one person to the next. Differentiations in brain structure and function have been observed between autistic and typically developing brains. Various aspects of brain function, including information processing, sensory perception, and the capacity to interpret and respond to social cues, may be affected by these differences. The signs and symptoms of ASD are readily apparent to parents in the first few years of their child’s life. The symptoms of ASD, it is said, first appear in early childhood and continue into maturity. Brain MRI analysis utilizing deep learning methods show encouraging findings in the study of ASD. In this study, we look at how well a customized VGG16 architecture compares models that have already been trained. Inception v3, ResNet50, DenseNet121, and MobileNet are used to identify and analyze ASD problems in a brain MRI dataset from people with ASD. The proposed methods have been vali-dated on the unlabeled ABIDE 1 dataset that is freely available to the public. After the unlabeled data has been clustered, five different Deep Learning (DL) methods are used and compared. The results are compared to those of other studies that have already been conducted.

Sleep-disordered breathing is a significant problem affecting the pediatric population. These conditions can affect sleep quality and significantly affect children's overall health and well-being. Difficulties in social interaction, communication, and repetitive behavioral patterns characterize autism spectrum disorder. Sleep disturbances are common in children with ASD. This literature review aims to gather and analyze available studies on the relationship between SDB and children with autism spectrum disorder. We comprehensively searched the literature using major search engines (PubMed, Scopus, and Web of Science). After removing duplicates, we extracted a total of 96 records. We selected 19 studies for inclusion after a thorough title and abstract screening process. A total of 7 articles were ultimately included. This review has analyzed the relationship between autism spectrum disorder and sleep-disordered breathing, particularly Obstructive Sleep Apnea, highlighting an intriguing web of complex associations. Some studies involving children have demonstrated a significant association between autism spectrum disorder and the presence of Obstructive Sleep Apnea. Furthermore, a heightened risk of developing sleep disturbances, including sleep-disordered breathing, has emerged in children with autism. The risk and prevalence of obesity are increasing in pediatric subjects with autism spectrum disorder. Obesity has been identified as a predictive factor for Sleep-disordered breathing, and Body Mass Index can directly correlate with Obstructive Sleep Apnea in these children. Adenotonsillectomy has proven to be pivotal in improving behavioral issues in autism spectrum disorder children with obstructive Sleep Apnea. In conclusion, this review underscores the complexity of the interplay between autism spectrum disorder and sleep-disordered breathing, emphasizing the importance of further research to understand underlying mechanisms and develop optimal therapeutic and preventative approaches to enhance sleep quality and overall health in children with autism spectrum disorder.

Children with history of prematurity are at higher risk of complications, comorbidities and iron deficiency. In this study we assess the prevalence of restless sleep disorder, as well as that of periodic leg movements during sleep (PLMS) in these children. Retrospective chart review of sleep studies in children aged 1-18 years, with history of prematurity. Only diagnostic studies in children without diagnosis of a genetic syndrome or airway surgery or tracheostomy were included. Three groups were compared, children with PLMS index&gt;5, children with restless sleep disorder (RSD), and children with neither elevated PLMS index nor RSD. During the study, 2,577 sleep studies were conducted. Ninety-two studies fit our criteria and were included in analysis. Median birth age was 31 weeks, interquartile range (IQR) 27-34 weeks. Thirty-two (34.8%) children were referred for restless sleep and 55 (59.8%) for snoring; after polysomnography 18% were found to have PLMS index &gt;5/hour, 14% fit the criteria for RSD. There were no statistically significant differences in polysomnographic parameters between the children with RSD, PLMS and the remaining group, except for lower obstructive apnea/hypopnea index (Kruskal-Wallis ANOVA 8.621, p=0.0135) in the RSD group (median 0.7, IQR 0.3-0.9) than in the PLMS (median 1.7, IQR 0.7-3.5), or than in the nonRSD/nonPLMS group (median 2.0, IQR 0.8-4.5). There was elevated frequency of RSD and elevated PLMS in children with history of prematurity that might be linked to the increased risk of iron deficiency in premature infants. These new results add new knowledge on the prevalence of RSD in these children.

Study Objectives: To (1) investigate the prevalence of sleep disorder symptoms in caregivers of children with autism spectrum disorder (ASD) and (2) the relationships between caregiver sleep problems and their health-related quality of life and family quality of life. Methods: Descriptive cross-sectional study of caregivers (N=62) of children ages 6 to 11 years old diagnosed with ASD receiving care at a regional autism research and resource center. Measurements and Results: Participants completed the Sleep Habits Questionnaire (SHQ), the Medical Outcomes Study (MOS) SF-12, and the Beach Center Family Quality of Life Scale (FQOL). Caregivers with longer sleep duration reported better mental health and better family quality of life. Caregivers who reported insomnia symptoms, non-restorative sleep, and insufficient sleep were more likely to report poorer mental health than caregivers who did not report these sleep disorder symptoms. Caregivers with obstructive sleep apnea and restless legs syndrome experienced worse physical quality of life. Conclusions: The physical and mental health of the primary caregiver is essential to the support of the child with ASD and to the functioning of the family. The study findings point to the importance of future research and interventions to enhance sleep health in order to improve quality of life for caregivers of children with ASD.

Zoom dysmorphia (ZD) is a facial dysmorphia triggered or aggravated by frequent virtual meetings. The frequent use of videoconferencing platforms has been linked to a distorted perception of facial images as individuals have an increased awareness of their appearance, given constant video feedback. As a result, dysmorphic concerns can develop. It is crucial to identify ZD as the condition interferes with the individual’s life and can trigger or aggravate body dysmorphic disorder (BDD). A standardized approach for screening ZD in non-psychiatric settings has yet to be defined. We discuss the features of ZD and the challenges of screening for ZD in a virtual setting. To facilitate the recognition of ZD in telehealth consultations, we propose a comprehensive ZD screening questionnaire that includes questions related to typical ZD features and a BDD-focused question. The questionnaire is concise and allows the identification of individuals with a potential ZD. A BDD assessment in such individuals should follow.

In Switzerland, psychomotor therapy (PMT) is a standard treatment for children with graphomotor impairments, but scientific evidence of its effectiveness is rare. To investigate the effectiveness of PMT, we conducted a randomized field trial (RFT). The sample consisted of 121 first and second graders with graphomotor impairments, some of whom met the criteria of developmental coordination disorder, while the remaining suffered from developmental dysgraphia. The treatments lasted over 5 months. Handwriting fluency and consistency were measured five times on a digitizing tablet. All participating children completed a self-concept interview, and a standardized fine motor performance test twice. Psychomotor therapy significantly improved the fine motor skills of the therapy group compared to those of the waiting group. However, there was no evidence that the treated children improved more than the waiting children in terms of their graphomotor skills such as frequency, automaticity, and consistency of forming letters. Finally, the children of the therapy group showed partial improvements in their handwriting self-concept, while those of the waiting group children remained stable. This short-term RFT demonstrated the effectiveness of PMT in terms of fine motor skills and some aspects of the handwriting self-concept but showed no effects on handwriting fluency and consistency.

This paper presents a framework to recognize the affective state of children with Autism Spectrum Disorder (ASD) in an in-the-wild setting using Heart Rate (HR) information. Our algorithm classifies a child’s emotion into positive, negative, or neutral states by analyzing the heart rate signal. The HR signal is obtained from a smartwatch in real-time using our smartwatch application. The heart rate data is acquired when the child learns to code a robot while interacting with an avatar that assists the child in communications skills and programming the robot. In this paper, we also present a comparison of using HR data for the classification of emotions with classification based on features extracted from HR signals using Discrete Wavelet Transform (DWT). Our experimental results show that the proposed method produces a comparable performance with the state-of-the-art HR-based emotion recognition techniques, despite the fact that our experiments are performed in an uncontrolled setting as opposed to a lab environment. This work contributes to real-world affect analysis of children with ASD using HR information.

ADHD, a neurodevelopmental condition, is distinguished by a triad of symptoms including inattention, hyperactivity, and impulsivity. It is frequently accompanied by comorbidities such as anxiety, depression, and learning disabilities. As a result, clinicians often face challenges in accurately diagnosing ADHD and differentiating it from other conditions. As a prospective remedy for ADHD, scientists have investigated Transcranial Direct Current Stimulation (tDCS), a non-invasive technique of stimulating the brain. It involves applying low-intensity electrical currents to specific regions of the brain to modulate neural activity. This review paper aims to provide an overview of the comorbidities associated with ADHD and the differential diagnosis of the condition. It also explores the potential of tDCS as a treatment option for ADHD, including its mechanisms of action and efficacy in improving ADHD symptoms. The comorbidities discussed in this review include anxiety disorders, mood disorders, and substance use disorders. These comorbidities are frequently seen in individuals with ADHD and can complicate the diagnosis and treatment of the condition. The paper also highlights the importance of considering comorbidities when assessing ADHD, as well as the potential impact of these conditions on treatment outcomes. The differential diagnosis section of the paper explores conditions that can present with symptoms similar to ADHD, such as anxiety disorders, mood disorders, and learning disabilities. It emphasizes the need for a thorough assessment and differential diagnosis to identify ADHD and differentiate it from other conditions accurately. The final section of the paper discusses the potential of tDCS as a treatment option for ADHD. It examines the mechanisms of action of tDCS and its efficacy in improving ADHD symptoms, including attention, hyperactivity, and impulsivity. It also explores the potential for tDCS to improve comorbid conditions associated with ADHD. Overall, this review provides a comprehensive overview of comorbidities and differential diagnoses in ADHD and the potential of tDCS as a treatment option. The paper highlights the importance of a thorough assessment and personalized treatment plan for individuals with ADHD, particularly those with comorbidities.

Borderline Personality Disorder (BPD) is a severe personality disorder whose neural bases are still unclear. Indeed, previous studies reported inconsistent findings concerning alterations in cortical and subcortical areas. In the present study we applied for the first time an unsupervised machine learning approach known as mCCA+jICA, in combination with a supervised machine learning approach known as Random Forest, to possibly find covarying GM-WM circuits that separate BPD from controls and that are also predictive of this diagnosis. To this aim, we analyzed the structural images of patients with BPD and matched HCs. Results showed that two GM-WM covarying circuits, including basal ganglia, amygdala, and portions of the temporal lobes and of the orbitofrontal cortex, correctly classified BPD against HC. Notably, these circuits are affected by specific child traumatic experiences (emotional and physical neglect, and physical abuse) and predict symptoms severity in the interpersonal and impulsivity domains. These results support that BPD is characterized by anomalies in GM and WM circuits related to early traumatic experiences and specific symptoms.