This paper develops a multi-task learning framework that attempts to incorporate the image structure knowledge to assist image inpainting, which is not well explored in previous works. The primary idea is to train a shared generator to simultaneously complete the corrupted image and corresponding structures --- edge and gradient, thus implicitly encouraging the generator to exploit relevant structure knowledge while inpainting. In the meantime, we also introduce a structure embedding scheme to explicitly embed the learned structure features into the inpainting process, thus to provide possible preconditions for image completion. Specifically, a novel pyramid structure loss is proposed to supervise structure learning and embedding. Moreover, an attention mechanism is developed to further exploit the recurrent structures and patterns in the image to refine the generated structures and contents. Through multi-task learning, structure embedding besides with attention, our framework takes advantage of the structure knowledge and outperforms several state-of-the-art methods on benchmark datasets quantitatively and qualitatively.

Visual question answering (VQA) is receiving increasing attention from researchers in both the computer vision and natural language processing fields. There are two key components in the VQA task: feature extraction and multi-modal fusion. For feature extraction, we introduce a novel co-attention scheme by combining Sentence-guide Word Attention (SWA) and Question-guide Image Attention (QIA) in a unified framework. To be specific, the textual attention SWA relies on the semantics of the whole question sentence to calculate contributions of different question words for text representation. For the multi-modal fusion, we propose a “Cross-modal Multistep Fusion (CMF)” network to generate multistep features and achieve multiple interactions for two modalities, rather than focusing on modeling complex interactions between two modals like most current feature fusion methods. To avoid the linear increase of the computational cost, we share the parameters for each step in the CMF. Extensive experiments demonstrate that the proposed method can achieve competitive or better performance than the state-of-the-art.

This article presents multiple novel and compelling arguments to systematically disprove two widely accepted notions: one is that entropy is a measure of disorder, and the other is Schrödinger's notion of negative entropy (negentropy). These two notions have been cited hundreds of times annually in scientific literature for decades. To mathematically describe the second law of thermodynamics, the concept of entropy was created and defined in the 1860s as heat energy divided by thermodynamic temperature, and the entropy of an object increases when it absorbs heat. This is the uncontroversial and fundamental nature of entropy, which is pivotal to current thermodynamics and from which multiple facts are deduced. These facts consistently disprove the above two notions. Some biological facts, such as the one that life orderliness is encoded and provided by inherited genes rather than entropy decline, can also disprove Schrödinger's negentropy notion. Moreover, this article further clarifies the reasoning errors of the two notions. It also shows that the tendency of many natural or social systems to become more disordered over time does not result from the second law of thermodynamics.

Many people in the USA have lost their lives or become addicted to opioids via prescription opioids given for chronic pain. The chronic pain epidemic has emerged due to a convergence of factors, including the medicalization of pain as well as injuries happening in the workplace, in the home, or during recreation. Opioid prescriptions rose precipitously from the late 1990’s through to approximately 2020, and despite public awareness, still are increasing. This review examines literature on integrative approaches to chronic pain as delineated by pain type. The review is organized by the most common causes of chronic pain, and randomized controlled trials, meta-analyses and systematic reviews are extracted for each cause of chronic pain. Several promising interventions may alleviate chronic pain of some causes, and some interventions may work across pain causes. Lowering inflammation through dietary or lifestyle regimens may be a general way of reducing pain. Pain can be alleviated through several adjunctive and integrative treatment approaches, which may serve to lower the need for opioid medication.

Borderline Personality Disorder (BPD) is a complex and severe mental disorder. It is associated with greater use of medical services. It is characterized by intense fear of abandonment, recurring suicidal thoughts or self-harm, paranoid ideation or dissociation, identity difficulties, chronic feelings of emptiness, impulsive behavior and unstable moods and relationships. BPD is one of the most prevalent personality disorders with a higher diagnosis in females. For a diagnosis of BPD in patients, they must have a chronic pattern of functional impairment in addition to five of nine listed DSM-5 criteria. This review was a narrative literature search between 2000 to 2023 with a focus on BPD comorbidities. Results: Researchers observed a complex pattern of comorbidities with BPD that connects with other externalizing and internalizing disorders. BPD has a high comorbidity rate with mood disorders including bipolar I and II disorders, major depressive disorder, anxiety disorders, panic disorder alcohol use disorder, nicotine dependence, marijuana dependance, posttraumatic stress disorder, other personality disorders, binge eating disorder and ADHD. Diagnosis and management of BPD in presence of some of these disorders are discussed.

Patients with bipolar disorder (BD) experience more frequent and intense emotions in response to environmental conditions relative to healthy subjects, which leads to mood instability. The aim of this study was to investigate the time-course of emotion regulation strategies, distraction, and reappraisal, in euthymic BD patients using electroencephalography (EEG). Fourteen BD patients and 13 matched healthy controls took part in an experiment constituted of three conditions, i.e., a passive viewing of positive, negative, and neutral pictures and two regulation conditions, one with reappraisal strategy and the other with distraction strategy. Critically, ERP results indicated that during passive viewing, the Late Positive Potential (LPP) was larger in BD patients compared with healthy controls, but only for neutral pictures. During emotion regulation, LPP amplitude was reduced in distraction condition compared with viewing one, especially for negative emotions in both patients and controls. Importantly, LPP was reduced in reappraisal condition compared with passive viewing in an early time window for negative emotions and in a later time window for positive emotions in controls but not in patients. Our findings showed that the neurochronometry of emotion regulation is faster for negative than for positive emotion in controls but not in BD patients.

The high demands of caring for and raising a child with autism spectrum disorder on a daily basis fatally lead parents to the appearance of physical and mental fatigue. This study aimed to assess the effect of social support and spirituality on the fatigue of parents with children with autistic spectrum disorder. A cross-sectional study with a convenience sample was conducted in Schools of Special Education in Attica (Greece). The sample consisted of 123 parents who completed The Fatigue Assessment Scale (FAS), the Multidimensional Scale of Perceived Social Support (MSPSS), and the Functional Assessment of Chronic Illness Therapy Spiritual Well-Being Scale (FACIT Sp-12) to measure the levels of fatigue, social support, and spirituality, respectively. Demographic data were, also, recorded. Data analysis was applied using IBM SPSS 21.0. The mean age was 47.3 years old, 81.3% were women, and 38.9% stated much / very much religious. The mean of total FAS was 28.4 (SD±7.5), of total FACIT Sp-12 was 31.5 (SD±8.7), and of total MSPSS 5.1 (SD±1.1). Higher levels of total MSPSS and FACIT Sp-12 were associated with lower total FAS (r= -0.50, p <0.001 and r= -0.49, p<0.001 respectively). Social support and spirituality are significant predictors of fatigue.

Background and Aims: Paramedics attend an unprecedented number of drug poisoning events daily in British Columbia (BC), Canada due to the ongoing public health crisis related to an increasingly toxic and unregulated supply of illicit drugs. Paramedics have the potential to support alternative models of care to reduce harm, but their perspectives towards harm reduction initiatives are polarized. Understanding the drug-related substance use content in paramedic curriculum documents is important for deploying effective harm mitigating programs. The objective of this study was to understand what drug-related substance use content currently exists within paramedic curriculum documents in British Columbia. Methods: We performed a document analysis of curriculum documents in BC’s paramedic training institutions, the primary program textbook, and the 2011 National Occupational Competency Profile (NOCP). We used the Checklist for Assessment and Reporting of Document Analysis (CARDA) tool to perform our analysis. We analysed and coded documents both inductively and deductively and subsequently combined, refined, and used the codes to inform the development of themes via reflexive thematic analysis. Results: Of the 44 documents analysed, 22 included codes relevant to the research questions. Paramedics are primarily taught to care for people who use drugs in an acute drug-poisoning response only, with little consideration of holistic care, and no meaningful mention of harm reduction. Some stigmatizing language was found within the content. Conclusions: Many opportunities to introduce holistic models of care for people who use drugs along the entire continuum of care are unaddressed by paramedic curriculum documents in BC. Curriculum developers should include people who have lived and living experience of drug use in the co-design of educational programs involving their care. Further qualitative analyses are required to evaluate the relationship between paramedic education and provider-based stigma.

Background Posttraumatic stress disorder (PTSD) is a complex condition that represents a significant burden in terms of individual disability and societal costs. Despite decades of research investigating treatment options, PTSD remains a major cause of quality-of-life impairment. We hypothesized that patients with PTSD might benefit from peripheral somatosensory stimulation (PSS) therapy. Methods Six adult patients with clinically diagnosed PTSD were enrolled to undergo daily PSS therapy over a 4-week period. Patients completed two surveys evaluating satisfaction with treatment and overall well-being (Survey 1) and severity of PTSD symptoms (Survey 2). Survey 1 was completed weekly during the course of the study. Survey 2 was based on the 9-question National Stressful Events Survey PTSD Short Scale (NSESSS) and was completed as a baseline prior to initiation of therapy and then at the conclusion of the trial. All data were analyzed by an independent statistician. Results Six male patients were enrolled in the study; all completed the trial. All patients demonstrated a decrease in symptoms within one week of initiation of therapy. This benefit was sustained and appeared to further improve over the course of the trial. For Survey 1, the overall median scores demonstrated a significant time-dependent improvement across measurement times (p < 0.001). For Survey 2, patients had statistically significant improvements from baseline regarding feelings of being emotionally upset and being overly alert. Results from a cumulative link mixed model demonstrated that treatment yielded a 38.2-fold higher likelihood of transitioning from a higher PTSD score at baseline to a lower (improved) score at week 4. No adverse events were described by the patients. Conclusions PSS stimulation appeared to improve symptoms in all six patients with PTSD symptoms in this trial. Patients had unanimous and clinically meaningful improvement in overall PTSD symptoms with treatment. By week 4, all patients responded that they enjoyed the treatment and would like to keep their device. We suggest that further investigation into the potential usefulness of PSS therapy in patients with PTSD is warranted.

The objective of this literature review was to provide an up-to-date overview, and an analysis of the current knowledge on narcolepsy. A systematic search was conducted in different databases to identify relevant studies on various aspects of narcolepsy. The search terms included "narcolepsy," "excessive daytime sleepiness," "cataplexy," and related terms. The search was limited to studies published up until 2022. The initial screening of studies was performed based on titles and abstracts to identify potentially relevant articles. Full-text assessment was then conducted to determine the eligibility of each study for inclusion in the review. Studies were included if they provided information on the symptoms, classification, genetic aspects, impact on daily life, and gaps in knowledge regarding narcolepsy. The review reveals several important findings regarding narcolepsy: 1. the classification of narcolepsy - Type 1 narcolepsy, previously known as narcolepsy with cataplexy, and Type 2 narcolepsy, also referred to as narcolepsy without cataplexy. 2. the genetic component of narcolepsy and the complex nature of the disorder, which is characterized by excessive daytime sleepiness, disrupted sleep patterns, and potential impacts on daily life activities and social functioning. 3. the important implications for clinical practice in the management of narcolepsy. Healthcare professionals should be aware of the different types of narcolepsies and their associated symptoms, as this can aid in accurate diagnosis and treatment planning. The review underscores the need for a multidisciplinary approach to narcolepsy management, involving specialists in sleep medicine, neurology, psychiatry, and psychology. Clinicians should consider the impact of narcolepsy on a person's daily life, including their ability to work, study, and participate in social activities, and provide appropriate support and interventions. There are several gaps in knowledge regarding narcolepsy. Future research should focus on further elucidating the genetic causes of narcolepsy and exploring potential biomarkers for early detection and diagnosis. Long-term studies assessing the effectiveness of different treatment approaches, including pharmacological interventions and behavioural therapies, are needed. Additionally, there is a need for research on strategies to improve the overall well-being and quality of life of individuals living with narcolepsy, including the development of tailored support programs and interventions.

Post-traumatic stress disorder (PTSD) is a debilitating mental health disorder that causes significant dysfunction in individuals. Currently, there are many approved Pharmacotherapy and Psychotherapy treatment options for PTSD, but unfortunately, half of the patients do not respond to traditional therapies. In this article, we reviewed clinical trials and research on 3,4-methylenedioxymethamphetamine (MDMA) assisted Psychotherapy in PTSD patients, its pharmacokinetics and current treatment guidelines for PTSD. Based on the results of the efficacy of MDMA assisted Psychotherapy from six phases II Randomized controlled trials. MDMA-assisted Psychotherapy for PTSD has received the “Breakthrough Therapy” designation from the FDA. MDMA can potentially reduce PTSD symptoms even in treatment-resistant cases by increasing certain neurohormones, i.e. dopamine, serotonin, norepinephrine and oxytocin. It also modulates activities in the brain regions involved in fear and anxiety. Future research is needed to show whether the advantages outweigh the disadvantages and whether its use can be integrated into available treatment options for PTSD.

Tourette syndrome is a complex neuropsychiatric condition that manifests in childhood and is often associated with other psychiatric comorbidities. This case describes a young male with Tourette syndrome with major depressive disorder and attention deficit hyperactivity disorder (ADHD), who experienced troublesome side-effects due to his existing medications (escitalopram, risperidone and methylphenidate). In order to control his tics, ameliorate depressive symptoms, and eliminate side-effects of stiffness and sedation, risperidone was switched to cariprazine, a third-generation antipsychotic medication with D3-D2 partial agonism. In addition, the antidepressant dose was also increased. With the new combination, the patient reported good control of his tics, together with significant improvement in depressive symptoms and no side-effects. Based on this case and the reviewed literature, cariprazine might be a viable option for patients with Tourette syndrome with other comorbid illnesses, whom are prone to side effects of medication.

Children with autism spectrum disorder (ASD) may have impaired vision from high refractive errors: near-sighted-myopia or far-sighted-hyperopia and aversion to spectacles or contact lenses. Refractive surgery can restore sharp vision and eliminate the need for spectacles or contact lenses. Restoration of sharp vision may improve ASD behaviors. Data were collated retrospectively for 267 children with refractive errors and neurodevelopmental disorders (NDD) diagnosed as ASD alone or NDD with ASD-like behaviors. Surgery was either laser photo-refractive keratectomy (PRK; 131 children), implantation of a phakic intra ocular lens (pIOL; 129 children) or removal of the crystalline lens and implantation of an IOL (refractive lens exchange, RLE; 21 children) under general anesthesia. Median age was 10.9 yrs. and median follow-up 3.1 yrs. Pre-operative refractive errors ranged from a mean +7.5 ± 0.9 D to -14.3. ± 4.8 D. Surgery corrected 87% to normal focal length (± 1 D). Visual acuity improved an average 0.6 logMAR. Social interactions and ASD behaviors improved in 55% (146/267) of the treated children (p &lt; .01), including school performance; motor skills; depth perception; repetitive or stereotyped movements; anxiety/fear; and attention. Refractive surgery improves both visual function and behaviors in the majority of highly near-sighted or far-sighted children with ASD.

Autism spectrum disorders (ASD) is a heterogeneous group of neurodevelopmental disorders characterized by food selectivity in a significant portion of the population. The nature of this selectivity remains unclear, with hypotheses suggesting associations with sensory disorders or stereotypical and repetitive patterns of activity and interests. This study aimed to determine the prevalence and nature of food selectivity traits in individuals with ASD compared to the neurotypical population. The study involved 219 participants, with 115 diagnosed with autism and 92 without. 12 children undergoing diagnosis were excluded from the analyses. Findings revealed that food selectivity traits are more common in individuals with ASD, with differences in preferences mainly involving structure, color, taste, and serving method. Children with ASD had more food selectivity traits than those without, and certain food characteristics could alter intake as they grow. Selectivity occurred for both sensory and stereotypical reasons, but stereotypical features significantly differentiated neurotypical individuals from those with ASD.

(1) Background: Many processes play a key role in how language functions, and these processes are strongly related to reading. The aim of our research was to explore the relationship between reading impairment associated with adult acquired cognitive-linguistic impairment (aphasia) and the components of verbal working memory involved in language functioning. (2) Methods: We measured the reading abilities of a total of twenty-two adults diagnosed with aphasia using the Adult Acquired Reading Assessment. To assess working memory, we applied the measurement procedures available in Hungarian for measuring verbal working memory. Correlation analyses were performed to investigate the relationship between reading and the components of working memory involved in processing verbal information. In order to explore this relationship as comprehensively as possible, the results were analysed quantitatively through group-level analyses and qualitatively through case studies. (3) Results: The results suggest that the functioning of phonological short-term memory and verbal working memory is crucial for the reading of vowels, for certain word reading sub-processes such as reading complex words and pseudowords, and for text comprehension tasks, in particular for the processing of implicit text-level infor-mation. (4) Conclusions: The data support a strong relationship between reading and working memory, but not all mechanisms are related with the same weighting. The data will contribute to the understanding of the relationship between reading and working memory, which is important in order to see which cognitive components are dominant in reading instruction and which other cognitive mechanisms are affected in poor readers.

Toxoplasma gondii (T. gondii) is one of the most common parasites that infect humans. The parasite exists in approximately 40 million people in the United States. Latent infections are frequently associated with tissue cysts of T. gondii in the skeletal muscle and brain tissue that can lead to mental disorders, congenital disorders, and vision dysfunction. Furthermore, self-directed violence, impulsivity, and aggression are associated with T. gondii infection. Dopamine is associated with human behavior including pleasure, aggression, memory, and substance use disorder; however, the result of the studies on the association of T. gondii infection and drugs of abuse are not well understood. The frequency of substance use may be associated with substance-induced modification of dopamine-receptor densities and basal dopaminergic activity. Likewise, T. gondii can directly or indirectly influence dopaminergic activity in infected cells. Based on this hypothesis, the current paper aimed to systematically review all published literature on the association between drugs of abuse and T. gondii infection. Systematic review of controlled studies on T. gondii infection and substance use effects in adults were searched on the electronic databases. Studies have shown that individuals infected with T. gondii display increased risky behavior, such as excessive alcohol consumption. Furthermore, it was observed that T. gondii seropositive subjects had a reduced likelihood of self-reported substance use compared to T. gondii seronegative subjects. This study confirms that SUD is a potential risk factor for behavioral and psychiatric complications associated with T. gondii infection. Ultimately T. gondii infection in the context of drug dependence and the development of SUD remain to be elucidated. Therefore, it is necessary to conduct further research characterizing the mechanisms associated with dopamine metabolism of drug dependence and withdrawal in the context of a T. gondii infection, evaluating the role of inflammation, and identifying potential drug-and-sex specific underpinnings of these associations.

Locomotor impairment is a high-prevalent and significant source of disability and significantly impacts a large population’s quality of life. Despite decades of research in human locomotion, the challenges of simulating human movement to study the features of musculoskeletal drivers and clinical conditions remain. Most recent efforts in utilizing reinforcement learning (RL) techniques are promising to simulate human locomotion and reveal musculoskeletal drives. However, these simulations often failed to mimic natural human locomotion because most reinforcement strategies have yet to consider any reference data regarding human movement. To address these challenges, in this study, we designed a reward function based on the trajectory optimization rewards (TOR), and bio-inspired rewards, which includes the rewards obtained from reference motion data captured by a single Intertial Moment Unit (IMU) sensor. The sensor was equipped on the participants’ pelvis to capture reference motion data. Also, we adapted the reward function by leveraging previous research in walking simulation for TOR. The experimental results showed that the simulated agents with the modified reward function performed better in mimicking the collected IMU data from participants, which means the simulated human locomotion was more realistic. Also, as this bio-inspired defined cost, IMU data enhanced the agent’s capacity to converge during the training process. As a result, the models’ convergence is faster than those developed without reference motion data. Consequently, human locomotion can be simulated more quicker and in a broader range of environments with a better simulation performance.

Cognitive function, arthrogryposis, obstetrics palsy, children, motor disorder

Recent research has suggested that working memory training interventions may benefit children with Developmental Language Disorder (DLD). The current study investigated a short and engaging adaptive working memory intervention that targeted executive skills and aimed to improve both language comprehension and working memory abilities in children with DLD. Forty-seven 6- to 10-year-old children with DLD were randomly allocated to an executive working memory training intervention (n=24) or an active control group (n=23). A pre-test/intervention/post-test/9-month-follow-up design was used. Outcome measures included assessments of language (to evaluate far transfer of the training) and working memory (to evaluate near transfer of the training). Hierarchical multiple regression analyses controlling for pre-intervention performance and age found group to be a significant predictor of sentence comprehension and of performance on six untrained working memory measures at post-intervention and 9-month follow-up. Children in the intervention group showed significantly higher language comprehension and working memory scores at both time points than children in the active control group. The intervention programme showed potential to improve working memory and language comprehension in children with DLD and demonstrated several advantages: it involved short sessions over a short period; caused little disruption in the school day; and was enjoyed by children.

Background: Body dysmorphic disorder (BDD) is a psychiatric disorder characterized by excessive preoccupation with imagined defects in appearance. Optical illusions induce illusory effects that distort the presented stimulus thus leading to ambiguous percepts. Using electroencephalography (EEG), we investigated whether BDD is related to differentiated perception during illusory percepts. Methods: 18 BDD patients and 18 controls were presented with 39 optical illusions together with a statement testing whether or not they perceived the illusion. After a delay period, they were prompted to answer whether the statement is right/wrong and their degree of confidence for their answer. We investigated differences of BDD on task performance and self-reported confidence and analysed the brain oscillations during decision-making using nonparametric cluster statistics. Results: Behaviorally, the BDD group exhibited reduced confidence when responding incorrectly, potentially attributed to higher levels of doubt. Electrophysiologically, the BDD group showed significantly reduced alpha power at mid-central scalp areas, suggesting impaired allocation of attention. Interestingly, the lower the alpha power of the identified cluster, the higher the BDD severity, as assessed by BDD psychometrics. Conclusions: Results evidenced that alpha power during illusory processing might serve as a quantitative EEG biomarker of BDD, potentially associated with reduced inhibition of task-irrelevant areas.

Proteolytic processing of amyloid precursor protein (APP) plays a critical role in pathogenesis of Azheimer’s disease (AD). Sequential cleavage of APP by β and γ secretases leads to generation of Aβ40 (non-amyloidogenic) and Aβ42 (amyloidogenic) peptides. Presenilin-1 (PS1) or presenilin-2 (PS2) pay a role of catalytic subunit of γ-secretase. Multiple familial AD (FAD) mutations in APP, PS1, or PS2 result in increased Aβ42:Aβ40 ratio and accumulation of toxic Aβ42 oligomers and plaques in patient brains. In this study we performed molecular modeling of APP complex with γ-secretase and analyzed potential effects of FAD mutations in APP and PS1. We noticed that all FAD mutations in APP transmembrane domain are predicted to cause an increase in the local disorder of its secondary structure. Based on structural analysis of known γ-secretase structures we proposed that APP can form a complex with γ-secretase in 2 potential conformations – M1 and M2. In conformation M1 transmembrane domain of APP forms a contact with perimembrane domain that follows the transmembrane domain 6 (TM6) in PS1 structure. In conformation M2 transmembrane domain of APP forms a contact with transmembrane domain 7 (TM7) in PS1 structure. By analyzing effects of PS1-FAD mutations on local protein disorder index, we discovered that these mutations increase conformational flexibility of M2 and reduce conformational flexibility of M1. Based on these results we proposed that M2 conformation, but not M1 conformation, of γ secretase complex with APP leads to amyloidogenic (Aβ42-generating) processing of APP. Our model predicts that APP processing in M1 conformation is favored by a curved membranes, such as membranes of early endosomes. In contrast, APP processing in M2 conformation is likely to be favored by a relatively flat memranes such as membranes of late endosomes and plasma membrane. These predictions are consistent with published biochemical analysis of APP processing at different subcellular locations. Our results suggest that specific inhibitors of Aβ42 production could be potentially developed by selectively targeting M2 conformation of γ secretase complex with APP.

The purpose of this study was to explore the association of cognition with hazardous drinking and alcohol related disorder in persons with bipolar disorder (BD). The study population included 1,268 persons from Finland with bipolar disorder. Alcohol use was assessed through hazardous drinking and alcohol related disorder including alcohol use disorder (AUD). Hazardous drinking was screened with the AUDIT-C (Alcohol Use Disorders Identification Test for Consumption) screening tool. Alcohol related disorder 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, or, reaction time (RT) test and the Paired Associative Learning (PAL) test. Association between RT-test and alcohol use was analyzed with log-linear regression, and eβ with 95% confidence intervals (CI) are reported. PAL first trial memory score was analyzed with linear regression, and β with 95% CI are reported. PAL total errors adjusted was analyzed with logistic regression and odds ratios (OR) with 95% CI are reported. After adjustment for age, education and housing status, hazardous drinking was associated with lower median and less variable RT in females while AUD was associated with a poorer PAL test performance in terms of the total errors adjusted scores in females. Our findings of positive associations between alcohol use and cognition in persons with bipolar disorder are unique.

Recently, ice with the stacking disorder structure, consisting of random sequences of cubic ice (Ic) and hexagonal ice (Ih) layers, is reported to be more stable than pure Ih/Ic. While, due to a much lower free energy barrier of heterogeneous nucleation, in practice, the freezing process of water is usually controlled by heterogeneous nucleation which is triggered by an external medium. Herein, molecular dynamic simulations were carried out to explore the polymorph dependence of ice on the lattice structure of substrates. It turns out that, during the nucleation stage, the polymorph of ice nuclei can be severely altered by the graphene substrate, on which the Ih was found to occupy an absolute majority in new-formed ice. This can be attributed to the structure similarity between graphene and basal face of Ih. Besides the nucleation stage, our results suggest that the substrate can not affect the polymorph of ice which is far from the graphene surface. The polymorph selectivity of graphene to Ih will diminish with the growth of ice layer.

Savouring is an emotion regulation strategy and intervention that focuses on the process of attending, intensifying and prolonging positive experiences and positive affect associated with these memories. Personal savouring involves a reflection on positive memories that are specific to the individual and do not involve others. In contrast, relational savouring entails reflecting on instances when people were responsive to the needs of their significant others. Such interventions hold potential to benefit parents of children with autism spectrum disorder (ASD). Mothers of children with ASD tend to experience higher anxiety, lower positive affect (PA) and more negative affect (NA) compared to mothers of neurotypical children, leading to a diminished overall well-being. Moreover, ASD has multiplied by up to four times in the recent decade. Thus, this paper investigates whether savouring may enhance the overall well-being of mothers of young children with ASD by increasing positive affect and decreasing negative affect. 52 mothers of neurotypical children and 26 mothers of children with ASD aged 3-7 years old were given a series of questionnaires and randomly assigned to either relational savouring or personal savouring conditions. In relational savouring, mothers were asked to reflect upon a shared positive experience with their child while in the personal savouring condition, a personal positive experience was recalled. Across mothers of children with ASD and neurotypical children, findings suggest that savouring leads to a decrease in NA (p &lt; .01) but not increases in PA. Similarly, mothers with higher levels of anxiety experience a greater decrease in NA (p &lt; .001) compared to mothers with lower levels of anxiety post-savouring. This study proposes that a brief savouring intervention may be effective among mothers of preschoolers. As lower levels of negative affect is linked to healthier psychological well-being, mothers might be able to engage in more effective and warm parenting after savouring exercises, which would cultivate positive mother-child relationships that benefit their children in the long-term.

COVID-19 induces coagulopathy at the base of SIC (sepsis-induced coagulopathy) and it is an important cause of death in the patients. Cytokine storm causes imbalance in coagulation and fibrinolytic system. A combination of hypercoagulability state, decrease or inhibition of fibrinolytic and endothelialopathy causes thromboembolic events. Underlined disease with a high rate of mortality in COVID-19 like diabetes, hypertension and some conditions like aging and obesity are the main disorders with hemostatic disturbance and increase of coagulopathy. Therefore, it seems that the combination of COVID-19 infection and these risk factors increase the risk of thromboembolic all together.

Background: CCL11 (eotaxin) is a chemokine with an important role in allergic conditions. Recent evidence indicates that CCL11 plays a role in brain disorders as well. Aims: This paper reviews the associations between CCL11 and aging, neurodegenerative, neuroinflammatory and neuropsychiatric disorders.Methods: Electronic databases were searched for original articles examining CCL11 in neuropsychiatric disorders.Results: CCL11 is rapidly transported from the blood to the brain through the brain-blood barrier. Age-related increases in CCL11 are associated with cognitive impairments in executive functions, episodic and semantic memory and, therefore, this chemokine was described as an “endogenous cognition deteriorating chemokine” (ECDC) or “accelerated brain-aging chemokine” (ABAC). In schizophrenia, increased CCL11 is not only associated with impairments in cognitive functions, but also with key symptoms including formal thought disorders. Some patients with mood disorders and premenstrual syndrome show increased plasma CCL11 levels. In diseases of old age, CCL11 is associated with lowered neurogenesis and neurodegenerative processes and, as a consequence, increased CCL11 increases risk towards Alzheimer's Disease. Polymorphisms in the CCL11 gene are associated with stroke. Increased CCL11 also plays a role in neuroinflammatory disease including multiple sclerosis. In animal models, neutralization of CCL11 may protect against nigrostriatal neurodegeneration. Increased production of CCL11 may be attenuated by glucocorticoids, minocycline, resveratrol and anti-CCL11 antibodies.Conclusion: Increased CCL11 production during inflammatory conditions may play a role in human disease including age-related cognitive decline, schizophrenia, mood disorders and neurodegenerative disorders. Increased CCL11 production is a new drug target in the treatment and prevention of those disorders.

Nanoparticles offer available tools for MDD research. In this assay, we applied CBR1 (cannabinoid receptor 1) knockout (CB1-/-) mice to study whether functionalized solid lipid nanoparticles loading with curcumin and dexanabinol (Cur/SLNs-HU-211) exhibited anti-depressant outcomes through CBR1. Wild-type (CB1+/+) animals together with CBR1 knockout (CB1-/-) animals received daily injections of Corticosterone (CORT) for 3 weeks to obtain MDD mice model, and then the therapeutic action of Cur/SLNs-HU-211 were evaluated, respectively. Our work show that Cur/SLNs-HU-211 nanoparticles in the existence of CBR1 facilitate an efficient motor function improvement in CORT-induced MDD mice model. Cur/SLNs-HU-211 nanoparticles alleviated symptoms on CB1+/+ MDD mice and resulted in dopamine and norepinephrine recovery following CORT-induced neurotoxicity. In conclusion, the possible mechanisms underlying the antidepressant effect of Cur/SLNs-HU-211 might be the induction of CB1 expression and downstream RASGEF1C and Egr1 expression, together with a significantly upregulation of neuron-specific genes in CB1+/+ mice only. In conclusion, CBR1 is necessary during the process of antidepressant activities of Cur/SLNs-HU-211 nanoparticles. This study confirms that Cur/SLNs-HU-211 nanoparticles based CBR1 in vivo targeting would be a potentially feasible and safe way to motivate future therapeutic strategies of Major Depressive Disorder.

Transdermal alcohol biosensors have the ability to detect the alcohol that emanates from the bloodstream and diffuses through the skin. However, they have suffered from long-term fouling of the sensor element and drift in the resulting sensor readings over time. Here, we report a disposable cartridge platform that solves the problem of sensor fouling, and an enzymatic detection pathway that minimizes baseline drift for sensitive detection. Laboratory characterization of the enzymatic alcohol sensor demonstrates a linear sensor range of between 0 and 50 mM. Further, we show continuous transdermal alcohol data recorded with a human subject over 48 hours.

Background: India has one-fifth of the world's population and the number of people suffering from mental illness is assumed to be huge considering the contribution of mental disorders to the overall burden of the disease being 13.9 %. Objectives of Study: To estimate prevalence and patterns of mental illnesses to assess the current mental health services and systems in the Madhya Pradesh. Material and Methods: Multi-stage, stratified, random cluster sampling technique, with random selection based on probability proportionate to size at each stage. A total of 3240 individuals aged 18 years and above were interviewed. Both quantitative and qualitative methods were employed. A set of 10 instruments including Mini International Neuro-psychiatric Interview were utilized. Results: The overall weighted prevalence for any mental illness was 16.7% lifetime and 13.9% current. Treatment-gap for all mental health problems is as high as 91% in the state along with huge socioeconomic impact of mental illness. Conclusions: This huge burden of mental, behavioural and substance use disorders, in Madhya Pradesh, calls for immediate attention of political leaders, policy makers, health professionals, opinion-makers and society at large. It is hoped that the data from the study will inform mental health policy and legislation and help shape mental health care delivery systems in the country.

The focus of this study was to investigate how individuals with bipolar disorder integrate auditory and visual speech information compared to non-disordered individuals and whether there were any differences in auditory and visual speech integration in the manic and depressive episodes in bipolar disorder patients. It was hypothesized that bipolar groups’ auditory-visual speech integration would be less robust than the control group. Further, it was predicted that those in the manic phase of bipolar disorder would integrate visual speech information more than their depressive phase counterparts. To examine these, the McGurk effect paradigm was used with typical auditory-visual speech (AV) as well as auditory-only (AO) speech perception on visual-only (VO) stimuli. Results. Results showed that the disordered and non-disordered groups did not differ on auditory-visual speech (AV) integration and auditory-only (AO) speech perception but on visual-only (VO) stimuli. The results are interpreted to pave the way for further research whereby both behavioural and physiological data are collected simultaneously. This will allow us understand the full dynamics of how, actually, the auditory and visual (relatively impoverished in bipolar disorder) speech information are integrated in people with bipolar disorder.

A probabilistic description is essential for understanding the dynamics of stochastic systems far from equilibrium, given uncertainty inherent in the systems. To compare different Probability Density Functions (PDFs), it is extremely useful to quantify the difference among different PDFs by assigning an appropriate metric to probability such that the distance increases with the difference between the two PDFs. This metric structure then provides a key link between stochastic systems and information geometry. For a non-equilibrium process, we define an infinitesimal distance at any time by comparing two PDFs at times infinitesimally apart and sum these distances in time. The total distance along the trajectory of the system quantifies the total number of different states that the system undergoes in time, and is called the information length. By using this concept, we investigate the information geometry of non-equilibrium processes involved in disorder-order transitions between the critical and subcritical states in a bistable system. Specifically, we compute time-dependent PDFs, information length, the rate of change in information length, entropy change and Fisher information in disorder-to-order and order-to-disorder transitions, and discuss similarities and disparities between the two transitions. In particular, we show that the total information length in order-to-disorder transition is much larger than that in disorder-to-order transition, and elucidate the link to the drastically different evolution of entropy in both transitions. We also provide the comparison of the results with those in the case of the transition between the subcritical and supercritical states and discuss implications for fitness.

The changing dynamics in climate is the primary and important determinant of agriculture productivity. The effects of this changing climate on overall productivity in agriculture can be understood when we study the effects of individual components contributing to the changing climate on plants and crops. Elevated CO₂ (eCO₂) and drought due to high variability in rainfall is one of the important manifestations of the changing climate. There is a considerable amount of literature that addresses climate effects on plant systems from molecules to ecosystems. Of particular interest is the effect of increased CO₂ on plants in relation to drought and water stress. As it is known that one of the consistent effects of increased CO₂ in the atmosphere is increased photosynthesis, especially in C₃ plants, it will be interesting to know the effect of drought in relation to elevated CO₂. The potential of elevated CO₂ ameliorating the effects of water deficit stress is evident from literature suggesting that these two are agents are brothers in arms protecting the plant from stress rather than partnering in crime, specifically water deficit when in isolation. The possible mechanisms by which this occurs will be discussed in this minireview. Interpreting the effects of short-term and long-term exposure of plants to elevated CO₂ in the context of ameliorating the negative impacts of drought will show us the possible ways by which there can be effective adaption to crops in the changing climate scenario.

The study uses annual time series data from the South Africa Reverse Bank (SARB) from 1980 to 2020 to examine the effectiveness of fiscal policy on economic growth in South Africa. The Augmented Dickey-Fuller (ADF) and Phillips-Perron (PP) unit root tests, as well as the Johansen Co-integration test, Granger causality test, and Vector Auto-Regression (VAR) method, were used in the study. Real GDP per capita (RGDP) is used as proxy of economic growth and gross fixed capital formation (GFCF), government expenditure (GEXP) and government deficit (GOVD) as the proxies of fiscal policy. The ADF test results show that all variables are stationary at the first difference, with the exception of GFCF and GEXP, which are stationary at I(0), whereas the PP test results show that all variables are stationary at I(1), with the exception of GEXP, which is stationary at I(0). At Maximum Eigenvalue, the four variables are not cointegrated. The findings of the Granger causality test demonstrated a unidirectional causation from GOVD to RGDP, as well as a bidirectional causality from RGDP to GFCF and GEXP. Error Correction Model Estimated using VAR shows that GFCF, GEXP have positive effect on RGDP whereas GOVD has a negative effect on RGDP in the short run. The findings also presented that the VAR's residuals are homoscedastic, which means they are normally distributed and have no serial correlation.

Autism Spectrum Disorder etiopathogenesis is still unclear and no effective preventive and treatment measures have been identified. Research has focused on the potential role of neuroinflammation and kynurenine pathway. Here we review the nature of these interactions. Pre-natal or neonatal infections would induce microglial activation, with secondary consequences on behavior, cognition and neurotransmitter networks. Peripherally, higher levels of pro-inflammatory cytokines and anti-brain antibodies have been identified. Increased frequency of autoimmune diseases, allergies, and recurring infections have been demonstrated both in autistic patients and in their relatives. Genetic studies, also, have identified some important polymorphisms in chromosome loci related to human leukocyte antigen (HLA) system. The persistence of immune-inflammatory deregulation would lead to mitochondrial dysfunction and oxidative stress, creating a self-sustaining cytotoxic loop. Chronic inflammation activates kynurenine pathway with increase in neurotoxic metabolites and excitotoxicity, causing long-term changes in glutamatergic system, trophic support and synaptic function. Furthermore, overactivation of kynurenine’s branch induces depletion of melatonin and serotonin worsening ASD symptoms. In this scenario, kynurenine pathway appears as a pharmacological target to treat and prevent ASD. Thus, in genetically predisposed subjects aberrant neurodevelopment may derives from a complex interplay between inflammatory process, mitochondrial dysfunction, oxidative stress and kynurenine pathway overexpression. To validate previous hypothesis a new translational research approach is necessary.

Ensuring the accuracy of wind power prediction is paramount for the reliable and stable operation of power systems. This study introduces a novel approach aimed at enhancing the precision of wind power prediction through the development of a multiscale hybrid model. This model integrates advanced methodologies including Improved Intrinsic Mode Function with Ensemble Empirical Mode Decomposition with Adaptive Noise (ICEEMDAN), Permutation Entropy(PE), Least Squares Support Vector Regression (LSSVR), Regularized Extreme Learning Machine(RELM), Multi-head Attention (MHA), and Bidirectional Gated Recurrent Unit (BiGRU). Firstly, the ICEEMDAN technique is employed to decompose the non-stationary raw wind power data into multiple relatively stable sub-modes, while concurrently utilizing PE to assess the complexity of each sub-mode. Secondly, the dataset is reconstituted into three distinct components: high-frequency, mid-frequency, and low-frequency, to alleviate data complexity. Following this, the LSSVR, RELM, and MHA-BiGRU models are individually applied to predict the high, mid,and low-frequency components, respectively. Thirdly, the parameters of the low-frequency prediction model are optimized utilizing the Dung Betele Optimizer(DBO) algorithm. Ultimately, the predicted results of each component are aggregated to derive the final prediction. Empirical findings illustrate the exceptional predictive performance of the multiscale hybrid model incorporating LSSVR, RELM,and MHA-BiGRU. In comparison to other benchmark models, the proposed model exhibits a reduction in Root Mean Square Error(RMSE) values of over 10%, conclusively affirming its superior predictive accuracy.

In this essay, I revisit Neural Networks (NNs) through the lens of evolutionary dynamics. From the two most important features of NNs, I recover the two most general equations of evolutionary dynamics. These two equations may thus serve as a connection between NNs and a body of knowledge that has been built over multiple centuries and has found application across a vast range of disciplines. This connection may therefore also help explain why NNs with the two features in question are applicable across a much broader range of domains than initially envisioned.

In high-traffic harbor waters, marine radar frequently encounters signal interference stemming from various obstructive elements, thereby presenting formidable obstacles in the precise identification of ships. To achieve precise pixel-level ship identification in the complex environments, a customized neural network-based ship segmentation algorithm named MrisNet is proposed. MrisNet employs a lightweight and efficient FasterYOLO network to extract features from radar images at different levels, capturing fine-grained edge information and deep semantic features of ship pixels. To address the limitation of deep features in the backbone network lacking detailed shape and structured information, an adaptive attention mechanism is introduced after the FasterYOLO network to enhance crucial ship features. To fully utilize the multi-dimensional feature outputs, MrisNet incorporates a Transformer structure to reconstruct the PANet feature fusion network, allowing for the fusion of contextual information and capturing more essential ship information and semantic correlations. In the prediction stage, MrisNet optimizes the target position loss using the EIoU function, enabling the algorithm to adapt to ship position deviations and size variations, thereby improving segmentation accuracy and convergence speed. Experimental results demonstrate that MrisNet achieves high recall and precision rates of 94.8% and 95.2%, respectively, in ship instance segmentation, outperforming various YOLO and other single-stage algorithms. Moreover, MrisNet has a model parameter size of 13.8M and real-time computational cost of 23.5G, demonstrating notable advantages in terms of convolutional efficiency. In conclusion, MrisNet accurately segments ships with different spot features and under diverse environmental conditions in marine radar images. It exhibits outstanding performance, particularly in extreme scenarios and challenging interference conditions, showcasing robustness and applicability.

The images captured by UAVs during inspection often contain numerous small targets related to transmission lines, which are critical and vulnerable elements for ensuring the safe operation of these lines. However, due to various factors such as the small size of the targets, low resolution, complex background, and potential line aggregation, achieving accurate and real-time detection becomes challenging. To address these issues, this paper proposes a detection algorithm called P2-ECA-EIOU-YOLOv5 (P2E-YOLOv5). Firstly, in order to address the challenges posed by the complex background and environmental interference that impact small targets, an ECA attention module is integrated into the network. This module effectively enhances the network's focus on small targets while concurrently mitigating the influence of environmental interference. Secondly, considering the characteristics of small target size and low resolution, a new high-resolution detection head is introduced, which is more sensitive to small targets. Lastly, the network utilizes the EIOU-Loss as the regression Loss function to improve the positioning accuracy of small targets, as they tend to aggregate. Experimental results demonstrate that the proposed P2E-YOLOv5 detection algorithm achieves an accuracy P of 96.0% and an average accuracy (mAP) of 97.0% for small target detection in transmission lines.

Infrared detection plays an important role in the military, aerospace, and other fields, which has the advantages of all-weather, high stealth, and strong anti-interference. However, infrared dim small target detection suffers from complex backgrounds, low signal-to-noise ratio, blurred targets with small area percentages, and other challenges. In this paper, we proposed a multiscale YOLOv5-AFAM algorithm to realize high-accuracy and real-time detection. Aiming at the problem of target intra-class feature difference and inter-class feature similarity, the Adaptive Fusion Attention Module - AFAM was proposed to generate feature maps that are calculated to weigh the features in the network and make the network focus on small targets. This paper proposed a multiscale fusion structure to solve the problem of small and variable detection scales in infrared vehicle targets. In addition, the downsampling layer is improved by combining Maxpool and convolutional downsampling to reduce the number of model parameters and retain the texture information. For multiple scenarios, we constructed an infrared dim and small vehicle target detection dataset, ISVD. The multiscale YOLOv5-AFAM was conducted on the ISVD dataset, compared to YOLOv7, mAP@0.5 achieves a small improvement while the parameters are only 17.98% of it. By contrast with the YOLOv5s model, mAP@0.5 was improved by 4.3% with a 6.6% reduction in the parameters. Experiments results demonstrate that the multiscale YOLOv5-AFAM has a higher detection accuracy and detection speed on infrared dim and small vehicles.

3D point cloud classification tasks have been a hot topic in recent years. Most existing point cloud processing frameworks lack context-aware features due to the deficiency of sufficient local feature extraction information. Therefore, we design an augmented sampling and grouping (ASG) module to efficiently obtain fine-grained features from the original point cloud. In particular, this method strengthens the domain near each centroid and makes reasonable use of the local mean and global standard deviation to mine point cloud’s local and global features. In addition to this, inspired by the transformer structure UFO-ViT in 2D vision tasks, we first try to use a linearly-normalized attention mechanism in point cloud processing tasks, investigating a novel transformer-based point cloud classification architecture UFO-Net. An effective local feature learning module is adopted as a bridging technique to connect different feature extraction modules. Importantly, UFO-Net employs multiple stacked blocks to better capture feature representation of the point cloud. Extensive ablation experiments on public datasets show that our method outperforms other state-of-the-art methods. For instance, our network performed with 93.7% overall accuracy on the ModelNet40 dataset, which was 0.5% higher than PCT. Our network also archived 83.8% overall accuracy on the ScanObjectNN dataset, which is 3.8% better than PCT.

Measuring pulmonary nodules accurately can help with early diagnosis of lung cancer, which can improve a patient’s chances of survival. Many methods for segmenting nodules have been developed, but they all rely on input from radiologists in the form of a 3D volume of interest (VOI) or use a constant region of interest (ROI) and only consider the presence of nodule within the given VOI. These approaches limit the networks’ ability to detect nodules outside the given VOI and can also include unnecessary structures in the VOI, leading to potentially inaccurate segmentation. In this work, we propose a novel approach for 3D lung nodule segmentation by using 2D region of interest (ROI) inputted from radiologist or computer-aided detection (CADe) system. Particularly, we design a dual-encoder-based hard attention network (DEHA-Net) which incorporates the full slice of thoracic computed tomography scan along with the ROI mask to produce an accurate segmentation mask of lung nodule in the given slice. The proposed architecture exploits the adaptive region of interest (A-ROI) algorithm to automatically investigates the penetration of lung nodule into surrounding slices while eliminating the need to drawing separate ROIs in each slice. Further, the framework performs the multi-view analysis, i.e., in sagittal and coronal views, to improve the segmentation performance. The proposed scheme has been rigorously evaluated on the lung image database consortium and image database resource initiative (LIDC/IDRI) dataset and an extensive analysis of results have been performed. The quantitative analysis shows that the proposed method not only improves the existing state-of-the-art in term of dice score but also, significantly robust against the different types, shape and dimensions of lung nodules.

The proliferation of deep learning in natural language processing (NLP) has led to the development and release of innovative technologies capable of understanding and generating human language with remarkable proficiency. Atinuke, a Transformer-based neural network, optimises performance across various language tasks by utilising a unique configuration. The architecture interweaves layers for processing sequential data with attention mechanisms to draw meaningful affinities between inputs and outputs. Due to the configuration of its topology and hyperparameter tuning, it can emulate human-like language by extracting features and learning complex mappings. Atinuke is modular, extensible, and integrates seamlessly with existing machine learning pipelines. Advanced matrix operations like softmax, embeddings, and multi-head attention enable nuanced handling of textual, acoustic, and visual signals. By unifying modern deep learning techniques with software design principles and mathematical theory, the system achieves state-of-the-art results on natural language tasks whilst remaining interpretable and robust.

Soybean are vulnerable to drought and temperature increases potentially induced by climate change. The purpose of this study was to determine if the use of cover crops (CC) can influence the gas exchange potential of glyphosate tolerant soybean when the vapor pressure deficit (Vpd) increases. This two years study was conducted in an open experimental field comprising Direct Seeding plots without CC (DS) or with CC (DSCC). Stomatal conductance (Gs) was measured five times on the same identified leaves following GBH application. These leaves were then collected in order to observe the stomata (size, density and stomatal index) with a scanning electron microscope and to perform measurements on foliar traits (venation density, width of midrib). The Vpd was calculated concomitantly to Gs measurements at the leaf surface. The results suggest that the use of CC promotes phenotypic change in soybean leaves (more elaborate venation and a higher stomatal density), which in turn may enhance their tolerance to drier conditions. In 2019, Gs could be up to 29% higher in DSCC plots compared to DS ones along with similar Vpd values. This study shows that the benefits of using CC can be observed through the morphological development strategies of the crop plants and their higher tolerance to drought.

This study presents a computational simulation to explore the distinctive patterns of neural connectivity in Autism Spectrum Disorder (ASD), focusing on the concepts of spatial and topological proximities. ASD is characterized by unique cognitive and behavioral profiles, often attributed to atypical neural development and connectivity. Our simulation employs a two-pronged approach: first, by modeling spatial proximity through the generation of clustered 3D node positions, we mimic the observed over-connectivity in localized brain regions in ASD. Second, we construct a topological network to represent the complex interplay of under-connectivity and over-connectivity in functional brain networks, a hallmark of ASD's neural landscape. The spatial model demonstrates pronounced clustering, reflecting the structural brain differences and atypical growth patterns reported in neuroimaging studies of individuals with ASD. In contrast, the topological model reveals a network with a mix of sparse and dense connections, simulating the diverse and often contradictory findings of functional connectivity in ASD, such as reduced long-range connections and increased local connections. Our simulation provides a visual and conceptual framework for understanding the altered neural connectivity in ASD. It underscores the importance of considering both physical (spatial) and functional (topological) aspects of brain connectivity to grasp the full extent of neurodevelopmental deviations in ASD. This approach not only aids in elucidating the neural underpinnings of ASD's core symptoms but also offers a foundation for developing targeted interventions. Future research, integrating more complex and individual-specific data, could further refine this model, enhancing our understanding of ASD's neural dynamics and contributing to personalized therapeutic strategies.

Intrinsically Disordered Proteins (IDPs) play crucial roles in signal transduction, cell differentiation, and transcriptional regulation. Many disrupted genes associated with mental disorders are identified as IDPs, and emerging evidence suggests the functional role of IDPs in neuropsychiatric disorders. However, few studies comprehensively explore the functional association between protein disorder properties and different mental diseases. To address this gap, we collected disrupted gene sets for seven mental diseases (MDD, SCZ, BP, ID, AD, ADHD, ASD) and a control gene set from normal brains using DisGeNET and GeneCards databases. The state-of-the-art predictor IUPred2A was then utilized to calculate the disorder properties , followed by a thorough comparison between the disordered proteins in mental diseases and healthy controls. Functional enrichment analysis and protein-protein interaction networks were conducted to investigate the IDPs’ functional roles in psychiatric diseases. Additionally, we demonstrated the role of IDPs in protein binding formations through a case study of alpha-synuclein using protein docking. Our findings reveal that differentially expressed proteins in mental disorders, especially in ASD and ADHD, exhibit more IDPs than those in healthy controls. IDPs in psychiatric disorders are significantly enriched in neurodevelopmental pathways and play an important role in in gene expression regulation. Our results indicate distinct functional patterns of disorder proteins among mental diseases and healthy controls. When comparing the IDPs between ASD and ADHD, we observed that IDPs in ADHD are more likely linked to the synaptic signaling and regulation, while IDPs in ASD are more tendentiously related to diverse and complex biological processes. Our work offers valuable insights into the important functions of IDPs in psychiatric disorders, enhancing our understanding of the molecular mechanisms involved in psychiatric disorders.

We investigated if the abnormal sleep-wake rhythms in infants are related to the future development of autism spectrum disorder (ASD), using questionnaire from September to October 2016. The parents of a total 160 children with ASD (male, n=123; female, n=37) recruited from the two hospitals in K and H cities and as a control group, 145 children (male, n=75; female, n=70) recruited from four nursery schools in T city. Fisher’s exact test was used for the comparison of categorical variables. The associations of bedtime and waking time on weekdays and holidays in infancy, at 1-3≦ (1-3) years of age and at 3-5 ≦ (3-5)years of age with the prevalence of ASD were analyzed using a multivariable logistic regression analysis. Especially by 3 years of age, the following factors were associated with an increased risk of developing ASD in the future: 1) short sleep (<8h), 2) long time to fall asleep (>60min), 3) sleep onset time after 22:00h, 4) wake-up time after 07:00h and/or 08:00h on weekdays and holidays, respectively, 5) frequent (>3 times) and long-term awakening (>60min). Misalignment and/or shift of the sleep-wake circadian rhythm in infant may be one of the precursors and/or risk factor to the future development of ASD.

Background Binge eating disorder (BED) is the most common eating disorder, categorized in DSM-V, but many patients are not diagnosed, because of the difficulty to consider BED distinct condition from obesity. Its identification is important for targeting individuals at high risk of obesity, adverse metabolic pattern, and cardiovascular disease. In this review we have highlighted the main aspects of screening, diagnosis and treatment of patients with BED and Obesity.

Developmental Coordination Disorder (DCD) is a neurodevelopmental condition characterized by non-progressive central motor impairments. Mild movement disorder features have been observed in DCD. Until now, the etiology of DCD remains unclear. Recent studies suggested a genetic substrate in some patients with DCD, but comprehensive knowledge about associated genes and underlying pathogenetic mechanisms is still lacking. In this study, we first identified genes described in literature in patients with a diagnosis of DCD according to the official diagnostic criteria. Second, we exposed the underlying pathogenetic mechanisms of DCD, by investigating tissue- and temporal gene expression patterns and brain-specific biological mech-anisms. Third, we explored putative shared pathogenetic mechanisms between DCD and frequent movement disorders with a known genetic component, including ataxia, chorea, dystonia, and myoclonus. We identified 12 genes associated with DCD in literature, which are ubiquitously expressed in the central nervous system throughout brain development. These genes are involved in cellular processes, neural signaling and nervous system development. There was a remarkable overlap (62%) in pathogenetic mechanisms between DCD-associated genes and genes linked with movement disorders. Our findings suggest that some patients might have a genetic etiology of DCD, which could be considered part of a pathogenetic movement disorder spectrum.

Myofascial pain is the most common cause of chronic pain in the masticatory region and can be assessed through clinical analysis and muscle palpation. Generally, it appears with headache and orofacial pain associated with sensitive points (trigger points) due to the excessive contraction of the masticatory muscle fibres. The study aims to evaluate how a correct treatment of myofascial pain can improve the life quality of the affected patients. In this case series, 300 patients with myofascial pain were divided into two groups: 150 with intra- and extra-articular disorder and 150 with the only extra-articular disorder. Each group included 75 males and 75 females. All the patients were treated with gnathological therapy through passive aligners and biofeedback exercises for 4 months. They underwent pain assessment (through a visual analogue scale and muscular palpation test) before, during and after the treatment, and nuclear magnetic resonance of the temporomandibular joint before and after the gnathological treatment. The treatment considerably reduced the pain in all patients, without drugs, in 4 months, according to the visual analogue scales and the palpation test. The temporomandibular magnetic resonance in each patient was similar before and after the gnathological treatment. The improvement in pain did not depend on a change in the relationship between the articular condyle and the disc.

The idealized rhombohedral unit cell of boron carbide is formed by a 12-atom icosahedron and a 3-atom linear chain. Phase transitions are second order and caused by the exchange of B and C sites or by vacancies in the structure. Nevertheless, the impact of such minimal structural changes on the properties can be significant. As the X-ray scattering cross sections of B and C atoms are very similar, the capability of X-ray fine structure investigation is restricted. Phonon spectroscopy helps closing this gap. Phase transitions known to date have been identified due to significant changes of properties: (1) The phase transition near the chemical composition B8C by clear change of the electronic structure; (2) The endothermic temperature-dependent phase transition at 712 K by the according change of specific heat; (3) The high-pressure phase transition at 33.2 GPa by the drastic change of optical appearance from opacity to transparency. These phase transitions affect IR- and Raman-active phonons and other solid-state properties. The phase transitions at B~8C and 712 K mean that a well-defined distorted structure is converted into another one each. In the high-pressure phase transition, an apparently well-defined distorted structure changes into a highly ordered one. In all these cases, the distribution of polar C atoms in the icosahedra plays a crucial role.

Autism spectrum disorder (ASD) is a common condition with lifelong implications and a strong hereditary component suggesting genetic underpinnings. The last decade has seen dramatic improvements in DNA sequencing, bioinformatics, and databases. We analyzed the raw DNA sequencing files on the Variantyx® (Framingham, MA, USA) bioinformatics platform for the last 50 autism patients evaluated with trio whole genome sequencing (trio-WGS). “Qualified” variants were defined as coding, very rare, and evolutionarily conserved. Primary Diagnostic Variants (PDV) additionally were in genes directly linked to ASD. A PDV was identified in 34/50 (68%) of cases, including 25 (50%) heterozygous de novo, 3 X-linked, 4 autosomal recessive, 2 autosomal dominant, and 1 heteroplasmic mtDNA variants. De novo variants in genes associated with ASD were far more likely to be Qualified than non-Qualified (control group, P = 0.002), validating that most are indeed disease causal. Only 14/34 (41%) of PDV cases had the variant listed on the laboratory report, and reanalysis increased diagnostic yield from 28% to 68%. The “missed” cases predominately included genes with zero (14 cases) to ≤5 prior reported case reports. Many cases both with and without a PDV had inherited Qualifying variants in known ASD-associated genes, suggesting polygenic inheritance. Thirty-three participants (66%) had treatment recommendation(s) based on DNA analyses. Our results demonstrate high yield of trio-WGS for revealing molecular diagnoses in ASD that is greatly enhanced by re-analyzing DNA sequencing files. Many are de novo and represent un/under-published conditions.

Febrile seizure (FS), which occurs as a response to fever, is the most common seizure that occurs in infants and young children. FS is usually accompanied by diverse neuropsychiatric symptoms including impaired social behaviors; however, no causative link between FS and autism spectrum disorder (ASD) has yet been established. Here, we provide evidence linking FS occurrence with ASD pathogenesis in rats. We developed an FS juvenile rats model and found ASD-like abnormal behaviors including deficits in social novelty, repetitive behaviors, and hyperlocomotion. In addition, FS model juvenile rats showed enhanced levels of gliosis and inflammation in the hippocampal CA2 region and cerebellum. Furthermore, abnormal levels of social and repetitive behaviors persisted in adults FS model rats. These findings suggest that occurrence of FS in infants can cause behavioral endophenotypes of ASD.

Background and objectives: Huntington disease (HD) is characterized by motor, cognitive and psychiatric manifestations and is caused by an expansion of CAG repeats over 35 triplets on the huntingtin (HTT) gene. However, expansions in the range 27-35 repeats (intermediate allele) can be associated with pathological phenotypes. The onset of HD is conventionally defined by the onset of motor symptoms, but psychiatric disturbances can precede the motor phase by up to twenty years. The aims of the present study are to identify HD patients in the pre-motor phase of the disease among patients diagnosed with bipolar disorders and to evaluate any differences between bipolar patients carrying normal HTT allele and patients with expanded HTT gene. Methods: We assessed the HTT genotype in an Italian cohort of 69 patients affected by bipolar disorder type 1 and type 2. Results: No patient was found to be a carrier of the pathological HTT allele, but 10% of bipolar subjects carried an intermediate allele. Carriers of the intermediate allele were older at onset of psychiatric symptoms than non-carriers. Conclusion: The pathological HTT gene was not associated with bipolar disorder, while we found a higher frequency of the intermediate allele among the bipolar population with respect to healthy controls.

Obesity has become a global epidemic, contributing to the development of numerous chronic diseases, including diabetes, chronic kidney disease (CKD) and cardiovascular disorders. MicroRNAs (miRNAs) have emerged as key regulators in various biological processes, including metabolism, inflammation, and tissue remodeling, making them pivotal players in obesity-related pathologies. This review aims to provide comprehensive insights into the roles of miRNAs in obesity, with a particular emphasis on their involvement in the pathogenesis of CKD and cardiovascular disorders. We highlight the involvement of specific miRNAs in adipose tissue development, energy homeostasis, inflammation, and insulin resistance, contributing to the pathogenesis of obesity. Moreover, we explore the impact of miRNAs on renal fibrosis, and inflammation, giving clues on their roles in the development and progression of CKD. Additionally, we discuss the influence of miRNAs on endothelial dysfunction, atherosclerosis, and cardiac remodeling, emphasizing their contribution to obesity-related cardiovascular disorders. Understanding the regulatory functions of miRNAs in these interconnected conditions holds promise for improved diagnosis, prognosis, and therapeutic interventions. Indeed, miRNAs are potential diagnostic biomarkers for obesity-related diseases although challenges remain to be elucidated before their clinical translation. Furthermore, we highlight the emerging strategies that target miRNAs as therapeutic interventions to mitigate the detrimental effects of obesity on kidney and cardiovascular health.

Sex/gender (S/G) differences in ASD language profiles have been poorly investigated. The present study aims to explore whether male (M) and female (F) children with ASD and with normal non-verbal cognitive abilities differ in their linguistic profiles. A sample of 76 Italian children with ASD (range: 4.9 – 8 years), including 50 M and 26 F, was retrospectively recruited. Language profiles were analyzed using standardized tests for the evaluation of receptive and expressive vocabulary as well as grammar. Grammatical comprehension was the most impaired domain compared to the other language measures in both M and F children. Comparing language profiles between S/G, F showed significantly better scores than M in grammatical production (p=.002), and M showed better active negative sentences comprehension (p=.035). Moreover, comparing the language profiles between M and F with receptive disorder, F had significantly worse grammatical comprehension and better grammatical production than M. Even among children without receptive disorder, F had significantly higher grammatical production scores. The S/G differences in language profile, particularly better expressive language in F than M, can partially contribute to the delayed ASD diagnosis or underdiagnosis of F without intellectual disability. Finally, our results document the importance of accurately investigating both expressive and receptive abilities in children with ASD.

Frontal incomes play an important role in human behavioral regulation and can be a determinant of eating behavior. The aim of this study was to analyse the frontal incomes in obese patients with and without Binge Eating Disorder (BED), compared to normoweight (NW) subjects and to analyse the effect of sex and binge disorder on quality of life, with age and BMI as covariates. A total of 114 participants comprised three different groups (obese with BED, obese without BED and NW) completed the Frontal Assessment Battery (FAB) and Impact of Weight on Quality of Life (IWQOL-lite). The results showed that obese patients, with and without BED, have poorer frontal functioning than NW persons. Obese patients with BED have lower performance in frontal income than obese patients without BED. Male participants have a higher perception of quality of life in all dimensions, with women showing lower values in self-esteem and sex life. Obese with BED show greater weaknesses in physical function. These results suggest that low frontal incomes and worse quality of life characterize obese patients, more evident in obese patients with BED.

Children with Autism Spectrum Disorder have deficits in social interaction and expressing and understanding emotions. Many robots for children with ASD have been proposed. However, few studies were found about how to design a social robot for children with ASD. Studies reviewed employed non-experimental studies to evaluate a social robot, but the methodology to design a social robot is rare. This study proposes a design path for a social robot for emotional communi-cation for children with ASD, following User-Centered Design approach. This design path is ap-plied with a study case, evaluated with an expert group on psychologist, human-robot interaction, human-computer interaction, and parents of children with ASD. Results show than following the design path proposed to design a social robot to communication emotions for children ASD is favorable.

To evaluate the impact of a new Substance Use Disorder (SUD) education program on medical students' representations, we selected the Substance Abuse Attitude Survey (SAAS) questionnaire, which we adapted to our curriculum and cultural context. To validate this adapted version, we conducted an exploratory factor analysis following the administration of our 29-items bSAAS questionnaire to 657 medical students in Belgium (response rate: 71,1%). 23 items correlated to 3 factors namely "Stereotypes and moralism", "Treatment optimism" and "Specialised treatment" were retained (70% of total variance explained, Cronbach's alpha= 0.80) and constituted the new questionnaire called beSAAS. The factor "Specialised treatment", stood out from previous studies which could be explained by our target population and the impact of the formal, informal, and hidden curriculum in medical education. This study highlighted certain factors influencing stereotypical representations such as age, gender, origin, and personal or professional experience with substance use. Our study allowed us to retain the beSAAS as a good questionnaire to evaluate SUD stigma and highlighted interesting findings to improve SUD training in medicine. Further studies are needed to complete its validity and reliability.

Background: To assess the prevalence of Hypertension (HTN) in Burning Mouth Syndrome (BMS) patients and to investigate its relationship with sociodemographic factors, pain and the psychological profile. Methods: A case-control study was conducted by enrolling 242 BMS patients and 242 controls matched for age and gender. Sociodemographic and clinical characteristics were recorded, and all the participants completed the numeric rating scale (NRS), the short-form of the McGill pain questionnaire (SF-MPQ), the Hamilton rating scale for anxiety and depression (HAM-A, HAM-D), the Pittsburgh sleep quality index (PSQI) and the Epworth sleepiness scale (ESS). Results: The BMS patients presented a statistically significant higher prevalence of HTN compared to the controls (55% versus 33.5%; p-value: &lt;.001**) and higher median scores of the NRS, SF-MPQ, HAM-A, HAM-D, PSQI and ESS (p &lt; .001**). Multivariate regression analysis in the BMS patients indicated positive correlations between HTN and age, systemic diseases, drug consumption and anxiety (p-value: &lt;.001**) and these predictors were responsible for 11.3% of the HTN variance in the BMS patients, when considered together. Conclusions: The prevalence of HTN was significantly higher in the BMS patients, since ageing, the presence of comorbidities, drug consumption and anxiety were potential predictors. Further studies are needed to better investigate the relationship between BMS and HTN.

Contemporary research has found that people with autism spectrum disorder (ASD) exhibit aberrant immunological function, with a shift toward increased cytokine production and unusual cell function. Microglia and astroglia were found to be significantly activated in immuno-cytochemical studies, and cytokine analysis revealed that the macrophage chemoattractant protein-1 (MCP-1), interleukin 6 (IL-6), tumor necrosis factor α (TNF-α), and transforming growth factor β-1 (TGFB-1), all generated in the neuroglia, constituted the most predominant cytokines in the brain. Taurine (2-aminoethanesulfonic acid) is a promising therapeutic molecule able to increase the activity of antioxidant enzymes and ATPase, which may be protective against aluminum-induced neurotoxicity. It can also stimulate neurogenesis, synaptogenesis, and reprogramming of proinflammatory M1 macrophage polarization by decreasing mitophagy (mitochondrial autophagy) and raising the expression of the markers of the anti-inflammatory and pro-healing M2 macrophages, such as macrophage mannose receptor (MMR, CD206) and IL-10, while lowering the expression of the M1 inflammatory factor genes. Taurine also induces autophagy, which is a mechanism that is impaired in microglia cells and is critically associated with the pathophysiology of the ASD. We hypothesize here that taurine could reprogram the metabolism of M1 macrophages that are overstimulated in the nervous system of people suffering from ASD, thereby decreasing the neuroinflammatory process, neuronal death, and improving cognitive functions. Therefore, we think that taurine can serve as an important lead for the development of novel drugs for the ASD treatment.

(1) Background: tuberculosis (TB) is considered one of the leading causes of death by a single in-fectious agent worldwide. This study aimed to identify hotspots of people diagnosed with tu-berculosis and abusive use of alcohol, tobacco and other drugs in communities through a geo-spatial intelligence application; (2) Methods: ecological study with a spatio-temporal approach. We considered tuberculosis cases diagnosed and registered in the Notifiable Diseases Information System, which presented information on alcoholism, smoking, and other drugs. Spatial Variations in Temporal Trends (SVTT) and scan statistics were applied for the identification of Hotspots; (3) Results: of 29.499 cases of tuberculosis were identified. And when we applied the STTT for al-coholism, three Hotspots were detected, one of which was protective (RR: 0.08 – CI95%: 0.02 – 0.32) and two at risk (RR:1.42 – CI95%: 1.11 – 1.73; RR: 1.39 - CI95%: 1.28 – 1.50). Regarding smoking, two risk clusters were identified (RR: 1.15 - CI95%: 1.01 – 1.30; RR: 1.68 - CI95%: 1.54 – 1.83). For other drugs, a risk cluster was found (RR: 1.13 - CI95%: 0.99 – 1.29) and two protections (RR: 0.70 - CI95%: 0.63 – 0.77; RR: 0.76 – CI95%: 0.65 – 0.89); (4) Conclusions: it was evidenced in the communities a problem of TB and drug addiction. Use disorder perhaps in a person's brain and behavior and leads to an inability to continue their treatment, putting the community at risk for TB.

The present study aimed to investigate the effect of a motor skills training program in children with DCD considering their gender. The Movement Assessment Battery for Children (MABC-2) classified the children and assessed skill changes over time. The study was implemented at four kindergartens in the Khorezm region of Uzbekistan. In the study, all children had DCD (5.17 ± 0.70 years; 10 girls), and all indicators on MABC-2 were less than 16 percent. Participants were divided into an experimental group (n=17) receiving ten weeks of motor skills training program for 45 min twice per week, the control group (n=7) proceeded with exercises of everyday living. All children in the experimental group had a significant increase in total percentile rank of MABC-2 and concerning each domain (manual dexterity; throwing and catching; balance). In the control group, a significant decrease in the total percentile rank of MABC-2 and each domain of MABC-2 was observed. The effectiveness of the intervention program was similar across both genders. The study supports that a period of 10 weeks of a motor skills training program can increase the quality of children's motor coordination and represent a valuable procedure for physical education specialists to enhance motor skills for children with DCD.

Term vascular dysfunctional refers to a wide spectrum of vascular abnormalities including pathogenesis of tumors, their proliferation and leading to malfunctioned conditions. The treatment of most of the vascular anomalies including peripheral arterial disease and cardiac disease is multi factor procedure which include the embolic therapy, laser-based treatments and coagulation are all plating important role in managing the disease associated with vascular breakdown. The research proposal defines the treatment and diagnosis procedures involved in treatment of vascular abnormalities with a deep emphasis on techniques, efficiency and complications resulted from various other procedure and use of mapping and sequencing techniques based on genetics of variants of selected genes PIk3CA which will ultimately be more effective.

Today, rupture of the anterior cruciate ligament is the most common heavy injury in athletes, but it can also occur in sedentary subjects. Its treatment, always adapted to the lifestyle, age and will of the patient, often remains surgical. However, despite its frequency and universality, there are still walking disorders following the operation. These disorders sometimes persist for several months, or even several years after surgery. The present study is therefore interested in determining the risk factors linked to walking disorders following reconstruction of the anterior cruciate ligament. The objective of this study is to determine what risk factors associated with walking disorders are after reconstruction of the anterior cruciate ligament by hamstring graft. This study focused on factors that may exist at 3 and 6 months postoperatively. Two populations participated in this study, a healthy population and a population having benefited from reconstruction of the anterior cruciate ligament. The operated test group is divided into two subgroups, one with a postoperative period of 3 months and the second at 6 months. All subjects were subjected to the same protocol, their gait on the treadmill was analyzed and evaluated with an Optogait® analysis system. A univariate analysis was carried out first. Then, a multivariate analysis by adjustment method was carried out in order to eliminate the potential confounding factors. The comparison of the results between the populations in the univariate analysis shows an absence of significant results however there are trends. The statistical results of the multivariate analysis showed interactions in the two subgroups of the operated population.In fact, it turns out that at 3 months there is a tendency to worse gait disorders in subjects with a high BMI reflecting overweight, when an associated surgical procedure has taken place on the meniscus, in subjects over 35 years of age and males. On the other hand, at 6 months, the tendency to worsening is visible only in the strata represented by subjects with a BMI corresponding to overweight, as well as in subjects having benefited from a meniscal suture associated with ACL reconstruction. .The results of the present study show that there are certain factors which tend to increase the risk of these walking disorders after reconstruction of the anterior cruciate ligament by hamstring graft, these factors are different depending on the time postoperative.

Sickle cell disease (SCD) is a blood disorder caused by a point mutation on the beta globin gene resulting in the synthesis of abnormal hemoglobin. Fetal hemoglobin (HbF) reduces disease severity, but the levels vary from one individual to another. Most research has focused on common variants which differ across populations and hence do not fully account for HbF variation. To investigate rare and common variants influencing HbF levels in SCD, we performed targeted next generation sequencing covering exonic and other significant fetal hemoglobin-associated loci, including BCL11A, MYB, HOXA9, HBB, HBG1, HBG2, CHD4, KLF1, MBD3, ZBTB7A and PGLYRP1. Results revealed a range of functionally relevant genetic variants. Notably, there were significantly more deletions in individuals with high HbF levels (11% vs 0.9%). We identified frameshift deletion in individuals with high HbF levels and frameshift insertions in individuals with low HbF. CHD4 and MBD3 genes, interacting in the same sub-network, were identified to have a significant number of pathogenic or non-synonymous mutations in individuals with low HbF levels, suggesting an important role of epigenetic pathways in the regulation of HbF synthesis. This study provides new insights in selecting essential variants associated with extreme HbF levels in SCD.

For the past several decades, abdominal prenatal ultrasonography has been the most significant technology in obstetrics with a long-established application. However, the frequency, exposure time, thermal and cavitation exposure indices, and increased acoustic output of the ultrasonic waves may be harmful to the embryo/fetus and might increase susceptibility to Autism Spectrum Disorder (ASD). The increase in the prevalence of ASD is associated with an affluent ethnicity, high socioeconomic status, and high parental education where prenatal ultrasonography is readily available and affordable. Enhanced biophysical adverse effects may link the analogous increase in prenatal ultrasonography and autism, and prenatal ultrasonography may emerge as a risk factor for autism. Radiography usage provides historical evidence for this fact: the predominant past opinion was that exposure to X-rays during pregnancy caused no significant risk to a fetus. However, the association between X-ray exposure and childhood leukemia was only established 40 years after X-ray use began. This review focuses on excessive PUS usage and ASD development. Public Abstract Advancements in medical technology over the past several decades have made prenatal ultrasound more frequently accessible to expecting mothers during their pregnancy, especially for the affluent. A parallel development in health care is the increase in autism diagnoses (Autism Spectrum Disorder, or ASD) in children of affluent families. There is a general lack of studies of the impact of prenatal ultrasound on fetuses, especially around varying attributes such as frequency, duration of exposure, and thermal and cavitation indices. There is also a historical precedent set, where exposing fetuses to X-rays was not found to be harmful until it was linked to the development of childhood leukemia decades later. This paper seeks to establish a need to further study these attributes of prenatal ultrasound overuse and their possible impact on a developing fetus, with a special focus on the occurrence of Autism.

Objective: To determine demographic predictors and comorbidities in hospitalized children with conduct disorder. Methods: A retrospective analysis was performed using Nationwide Inpatient Sample (2012–2014). All patients were ≤18 years and cases with primary diagnosis of conduct disorder (N = 32345) and a comparison group with another psychiatric diagnosis (N = 410,479) were identified using ICD-9-CM diagnosis codes. A logistic regression model was used to generate the Odds Ratio (OR) between both groups. Results: Children <11 years old have five times greater chances of admission for conduct disorder than adolescent (OR 5.339). African American males are more likely to be admitted for conduct disorder. Children with conduct disorder from low-income families have a 1.5 times higher likelihood for inpatient admission compared to high-income families. These children have about eleven times higher odds of comorbid psychosis (OR 11.810) and seven times for depression (OR 7.093) compared to the comparison group. Conclusion: Conduct disorders are more debilitating for children and families than many providers realize. African American male under 11 years is at the highest risk for inpatient management for conduct disorder. These patients have a higher risk of comorbid psychosis and depression which may further deteriorate the severity of illness and require acute inpatient care.

Autism spectrum disorder (ASD) refers to the diverse range of neurodevelopmental disorders accompanying impairments in social interaction, difficulties in communication, and stereotyped or repetitive behaviors. Unlike the older term, autism, the newer term, ASD, better reflects the broad range of autistic symptoms and denotes a single diagnostic category of autism accompanied by numerous conditions. The pineal hormone melatonin is a well-known neuroprotectant and circadian entrainer. This hormone crosses the placenta and enters the fetal circulation, then conveys photoperiodic information to the fetus during pregnancy. These actions enable normal sleep patterns and circadian rhythms, followed by normal neurodevelopment. Melatonin also reduces oxidative stress, which is harmful to the central nervous system. Therefore, melatonin acts as a neuroprotectant and circadian entrainer, and may reduce the risk of neurodevelopmental disorders such as ASD.

The segmentation of abnormal regions is vital in smart manufacturing. However, the existing segmentation system for detecting sauce-packet leakage on intelligent sensors encounters an issue of imaging blurring caused by uneven illumination. This issue adversely affects segmentation performance, thereby impeding the rapid production of industrial assembly lines. To alleviate this issue, we propose the two-stage Illumination-aware Sauce-packet Leakage Segmentation (ISLS) method for intelligent sensors. The ISLS comprises two main stages: Illumination-aware region enhancement and leakage region segmentation. In the first stage, YOLO-Fastestv2 is employed to capture the Region of Interest (ROI), which reduces redundancy computations. Additionally, we propose an image enhancement to relieve the impact of uneven illumination, enhancing the texture details of ROI. In the second stage, we propose a novel feature extraction network. Specifically, we propose the Multi-scale Feature Fusion Module (MFFM) and the Sequential Self-Attention Mechanism (SSAM) to capture discriminative representations of leakage. The multi-level features are fused by MFFM with a small number of parameters, which capture leakage semantics at different scales. The SSAM realizes the enhancement of valid features and the suppression of invalid features by adaptive weighting of spatial and channel dimensions. Furthermore, we generated a self-built dataset of sauce-packets, including 606 images with various leakage areas. Comprehensive experiments demonstrate that our ISLS method shows better results than several state-of-the-art methods, with additional performance analyses deployed on intelligent sensors to affirm the effectiveness of our proposed method. Our code is available at https://github.com/LSJ5106/SauceDetect.

Pavement crack detection is one of the key links in highway pavement maintenance management. In the current road crack detection, the network model easily ignores the shallow geometric features of cracks and cannot extract the key feature information of cracks. Aiming at the above problems, an EC-YOLOX network model is proposed. The CFPN is constructed to cross-fuse different scale features to solve the feature scale invariance. In the strengthen feature extraction layer, ECANet is fused to enhance the ability to pay attention to key information. The WEIoU loss function is proposed, which assigns different penalty terms to the target box in the vertical and horizontal directions. In terms of data, geometric distortion constraint correction is performed on the single-point perspective pavement image. The experimental data show that the accuracy of EC-YOLOX on the self-built data set reaches 76.34% mAP@0.5, which is 4.45% higher than that of YOLOX. The loss curve of EC-YOLOX is smoother than that of YOLOX, and the minimum training loss value is 2.136, which is 0.648 lower than the minimum loss value 2.784 of YOLOX. Many experiments have verified the effectiveness of EC-YOLOX in improving the detection effect in pavement crack detection.

The demand for precise indoor localization services is steadily increasing. Among various methods, fingerprint-based indoor localization has become a popular choice due to its exceptional accuracy, cost-effectiveness, and ease of implementation. However, its performance degrades significantly as a result of multipath signal attenuation and environmental changes. In this paper, we propose an indoor localization method based on fingerprints using self-attention and long short-term memory (LSTM). By integrating a self-attention mechanism and LSTM network, the proposed method exhibits outstanding positioning accuracy and robustness in diverse experimental environments. The performance of the proposed method is evaluated under two different experimental scenarios, which involve 2D and 3D moving trajectories, respectively. The experimental results demonstrate that our approach achieves an average localization error of 1.76 m and 2.83 m in the respective scenarios, outperforming the existing state-of-the-art methods by 42.67% and 31.64%.

Accurate estimation of Remaining Useful Life (RUL) for aircraft engines is essential for ensuring safety and uninterrupted operations in the aviation industry. Numerous investigations have leveraged the success of attention-based Transformer architecture in sequence modeling tasks, particularly in its application to RUL prediction. These studies primarily focus on utilizing onboard sensor readings as input predictors. While various Transformer-based approaches have demonstrated improvement in RUL predictions, their exclusive focus on temporal attention within multivariate time series sensor readings, without considering sensor-wise attention, raises concerns about potential inaccuracies in RUL predictions. To address this concern, our paper proposes a novel solution in the form of a two-stage attention based hierarchical transformer (STAR) frame-work. This approach incorporates a two-stage attention mechanism, systematically addressing both temporal and sensor-wise attentions. Furthermore, we enhance the STAR RUL prediction framework by integrate hierarchical encoder-decoder structures to capture valuable information across different time scales. By conducting extensive numerical experiments with the CMAPSS datasets, we demonstrate that our proposed STAR framework significantly outperforms current state-of-the-art models for RUL prediction.

In this paper, we present a groundbreaking methodology in the realm of aspect-level sentiment analysis, which capitalizes on the advanced capabilities of graph-based neural networks. Our approach, distinguished as the Aspect Correlation Graph Network (ACGN), represents a significant departure from conventional models. These traditional models often analyze aspects in isolation, failing to capture the intricate web of sentiment relationships that may exist within a single sentence. ACGN, however, is designed to address this gap by employing a sophisticated bidirectional attention mechanism, integrated with positional encoding. This unique combination not only enhances the model's ability to focus on relevant parts of the sentence but also aids in constructing detailed, aspect-focused representations. These representations are particularly crucial for understanding the nuanced interplay of sentiments associated with different aspects. Central to our model's architecture is the incorporation of a graph convolutional network. This network serves as a pivotal component in mapping and analyzing the complex network of sentiment correlations that can exist among various aspects within sentences. Through this integration, ACGN is able to unearth and interpret the subtle and often overlooked sentiment dynamics that traditional models might miss. Our comprehensive evaluations of the Aspect Correlation Graph Network, conducted using the SemEval 2014 datasets, have yielded promising results. These findings demonstrate a clear and significant advancement over the capabilities of existing models. Particularly, the results underscore the critical importance and utility of recognizing and understanding the connections between sentiments of different aspects in text analysis. This insight opens new avenues in the field of sentiment analysis, suggesting a broader application potential of ACGN in various contexts where understanding nuanced sentiment relationships is key. Overall, our study not only introduces a novel approach in aspect-level sentiment analysis but also sets a new standard for future research in this area. By highlighting the integral role of inter-aspect sentiment connections, ACGN paves the way for more sophisticated and accurate sentiment analysis tools, capable of handling the complexities of natural language with greater finesse and precision.

Traffic congestion and frequent traffic accidents have become main problems affecting urban traffic. Effective location prediction of vehicle trajectory can help alleviate traffic congestion, reduce the occurrence of traffic accidents, and optimize the urban traffic system. Vehicle trajectory is closely related to the surrounding Points of Interest (POI). POI can be considered as the spatial feature and can be fused with trajectory points to improve prediction accuracy. A Local Dynamic Graph Spatiotemporal- Long Short-Term Memory (LDGST-LSTM) is proposed in this paper to extract and fuse the POI knowledge and realize next location prediction. POI semantic information is learned by constructing the traffic knowledge graph, and spatial and temporal features are extracted by combining Graph Attention Network (GAT) and temporal attention mechanism. Moreover, the weights of POI that influence location prediction are visualized to improve the interpretability of the proposed model. The effectiveness of LDGST-LSTM is verified on two datasets, including Chengdu taxi trajectory data in October 2018 and August 2014. The accuracy and robustness of the proposed model are significantly improved compared with the benchmark models.

Underwater image processing faces significant challenges due to the absorption and scattering of light as it travels through water. A self-supervised learning network based on the self-attention mechanism is proposed for underwater visual applications in this paper. The goal is to improve the effectiveness and stability of feature extraction from underwater images. By incorporating self-attention mechanism, the sensitivity of the original network architecture to degraded features of blurred underwater images is enhanced. The network proposed in this paper is trained using transfer learning and evaluated on various underwater image datasets. With distributive, quantitative, and qualitative advantages compared with other methods, experimental results have demonstrated that the algorithm presented shows a slighter decline in feature extraction ability for blurred images as the turbidity of water increases.

Social communication skills, especially eye contact and joint attention, are highly impaired in autism spectrum disorder (ASD) and predict functional outcomes. Applied Behavior Analysis is one of the best evidence-based treatments for ASD, but it is not accessible to most families in low- and middle-income countries (LMICs) as it is a costly and intensive and needs to be delivered by highly specialized professionals. Parental training has emerged as an effective alternative. The aim of this study was to test the efficacy of a parental intervention group via video modeling to acquire eye contact and joint attention. Four graded measures of eye contact and joint attention (full physical prompt, partial physical prompt, gestural prompt, and independent) were assessed in 34 children with ASD and intellectual disability (ID). There was a progressive reduction in the level of prompt required over time to acquire eye contact and joint attention, and a positive correlation between the time of exposure to the intervention and acquisition of the abilities. This parent training using video modelling to teach eye contact and joint attention skills to children with ASD and ID is a low-cost intervention that can be applied in low- resource settings.

Using traditional methods based on detection rules written by human security experts, there is a significant challenge in accurately detecting many network threats that are increasingly becoming sophisticated. In order to deal with the limitation of the traditional methods, network threat detection techniques utilizing artificial intelligence technologies such as machine learning are being extensively studied. Research has also been conducted on analyzing various string patterns in network packet payloads through natural language processing techniques to detect attack intent. However, due to the nature of packet payloads containing binary data as well as text data, a new approach is needed that goes beyond typical natural language processing techniques. In this paper, we study a token extraction method optimized for payloads using n-gram and byte pair encoding techniques. Furthermore, we generate embedding vectors that can understand the context of the packet payload using algorithms such as Word2Vec and FastText. We also compute the embedding of various header data associated with packets, such as IP addresses and ports. Given the combination of these features, we ensemble a text 1D-CNN and a multi-head attention network in a novel fashion. We validated the effectiveness of our classification technique with the CICIDS2017 open dataset and over half a million data collected by The Education Cyber Security Center (ECSC) currently operating in South Korea. The proposed model showed remarkable progress compared to previous studies, demonstrating a highly accurate classification performance with an F1-Score of 0.998. Our model can also preprocess and classify 150,000 network threats per minute to help security agents in the field divert their time to analyzing more complex attack patterns.

Synthetic Aperture Radar (SAR) is an active sensor that uses microwave for sense, it is unrestricted by weather and illumination conditions, and it can observe targets all day and weather. Aircraft targets are important monitoring objects in military and civilian fields, and how to efficiently detect and recognize aircraft targets is an important topic in the field of SAR image interpretation. Based on the features of SAR images, such as complex background, high resolution, and multi-scale, we proposed an improved method based on YOLOv5s. Firstly, this paper proposed the structure of the multi-scale receptive field and channel attention fusion, which is applied to the shallow layer of the backbone of YOLOv5s, it can adjust the weights of the multi-scale receptive field during the training process to enhance the extraction ability of feature information. Secondly, we proposed four de-coupled detection heads to replace the original part in YOLOv5s, which can improve the efficiency and accuracy of SAR image interpretation for small targets. Thirdly, in the case of the limited amount of SAR images, this paper proposed multi methods of data augmentation, which can enhance the diversity and generalization of the network. Fourthly, this paper proposed the K-means++ to re-place the original K-means to improve the network convergence speed and detection accuracy. Finally, Experiments demonstrate that the improved YOLOv5s can enhance the accuracy of SAR image interpretation by 9.3%, and the accuracy of small targets is improved more obviously, reaching 13.1%.

Most natural language processing (NLP) tasks operate an input sentence as a sequence with token-level embeddings and features, despite its clausal structures. Taking Abstract Meaning Representation (AMR) parsing as an example, recent parsers are empowered by Transformers and pre-trained language models, but long-distance dependencies (LDDs) introduced by long sequences are still open problems. We argue that LDDs are not superficially blamed on the sequence length but are essentially related to the internal clause hierarchy. Typically, non-verb words in a clause cannot depend on words outside, and verbs from different but related clauses have much longer dependencies than those in the same clause. With this intuition, we introduce a type of clausal feature, hierarchical clause annotation (HCA), into AMR parsing and propose two HCA-based approaches, HCA-based self-attention (HCA-SA) and HCA-based curriculum learning (HCA-CL), to integrate HCA trees of complex sentences for addressing LDDs. We conduct extensive experiments on two in-distribution (ID) AMR datasets (AMR 2.0 and AMR 3.0) and three out-of-distribution (OOD) ones (TLP, New3, and Bio). Experimental results show that our HCA-based approaches achieve significant and explainable improvements against the baseline model and outperform the state-of-the-art (SOTA) model when encountering sentences with complex clausal structures that introduce most LDD cases.

Compressed Sensing (CS) MRI has shown great potential in enhancing time efficiency. Deep learning techniques, specifically Generative Adversarial Networks (GANs), have emerged as potent tools for speedy CS-MRI reconstruction. Yet, as the complexity of deep learning recon-struction models increases, this can lead to prolonged reconstruction time and challenges in achieving convergence. In this study we present a novel GAN-based model that delivers superior performance without escalating model complexity. Our generator module, built on the U-net architecture, incorporates dilated residual (DR) networks, thus expanding the network's receptive field without increasing parameters or computational load. At every step of the downsampling path, this revamped generator module includes a DR network, with the dilation rates adjusted according to the depth of the network layer. Moreover, we have introduced a channel attention mechanism (CAM) to distinguish between channels and reduce background noise, thereby fo-cusing on key information. This mechanism adeptly combines global maximum and average pooling approaches to refine channel attention. We conducted comprehensive experiments with the designed model using public domain MRI datasets of the human brain. Ablation studies af-firmed the efficacy of the modified modules within the network. Compared to other relevant models, our proposed model exhibits exceptional performance, achieving not only excellent sta-bility but also outperforming other networks in terms of Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index (SSIM). The model presents a promising pathway for enhancing the efficiency and quality of CS-MRI reconstruction.

Speech emotion recognition is a critical component for achieving natural human-robot interaction. The modulation-filtered cochleagram is a feature based on auditory modulation perception, which contains multi-dimensional spectral-temporal modulation representation. In this study, we propose an emotion recognition framework that utilizes a multi-level attention network to recognize emotions from the modulation-filtered cochleagram. The channel-level attention and spatial-level attention modules are used to capture emotional saliency maps of channel and spatial feature representations from the 3D convolution feature maps, respectively. Furthermore, the temporal-level attention module captures significant emotional regions from the concatenated feature sequence of the emotional saliency maps. Our experiments on the IEMOCAP dataset demonstrate that the modulation-filtered cochleagram significantly improves the prediction performance of categorical emotion compared to other evaluated features. Moreover, our emotion recognition framework achieves a better unweighted accuracy of 71% in categorical emotion recognition than several existing approaches in the experiments. In summary, our study demonstrates the effectiveness of the modulation-filtered cochleagram in speech emotion recognition, and our proposed multi-level attention framework provides a promising direction for future research in this field.

Effortful control (EC) describes individual differences in self-regulation, with a strong attentional basis. Moreover, during early childhood EC has a central role on children’s socio-emotional adjustment and academic achievement. The aim of the current study was to predict the development of EC at 36 months of age from early attentional and environmental measures taken in infancy using a machine learning approach. A sample of 75 infants participated in a longitudinal study running different waves of data collection at 6, 9 and 36 months of age. Attentional tasks were administered at 6 months of age and two additional measures were collected at 9 months of age. Parents reported household environment variables during wave 1, and their child’s EC at 36 months. A machine -learning algorithm was implemented to identify children with low EC scores at 36 months of age. An “attention only” model showed greater predictive sensitivity than the “environmental only” model. However, a model including both attentional and environmental variables was able to classify the groups (Low-EC vs Average-to-High EC) with 100% accuracy. Sensitivity analyses indicate that socio-economic variables together with attention control processes at 6-months, and self-restraint capacity at 9-months, are the most important predictors of EC. Results suggest a foundational role of executive attention processes in the development of EC in complex interaction with household environment and provide a new tool to identify early markers of socio-emotional regulation development.

Background & aims It is documented that low protein and amino-acid dietary intake is related to poorer cognitive health and increased risk of dementia. Degradation of the neuromodulatory pathways, (comprising the cholinergic, dopaminergic, serotoninergic and noradrenergic systems) is observed in neurodegenerative diseases and impairs the proper biosynthesis of key neuromodulators from micro-nutrients and amino acids. How these micro-nutrients are linked to neuromodulatory pathways in healthy adults is less studied. The Locus Coeruleus–Noradrenergic System (LC-NA) is the earliest subcortical structure affected in Alzheimer’s disease, showing marked neurodegeneration, but is also sensitive for age-related changes. The LC-NA system is critical for supporting attention and cognitive control, functions that are enhanced both by tyrosine administration and chronic tyrosine intake. The purpose of this study was to 1) investigate whether the dietary intake of tyrosine, the key precursor for noradrenaline (NA), is related to LC integrity 2) whether LC integrity mediates the reported association between tyrosine intake and higher cognitive performance (measured with Trail Making Test – TMT), and 3) whether LC integrity relates to an objective measure of brain maintenance (BrainPAD). Methods The analyses included 398 3T MRIs of healthy participants from the Berlin Aging Study II to investigate the relationship between LC integrity and habitual dietary tyrosine intake-daily average (HD-Tyr-IDA). As a control procedure, the same analyses were repeated on other main seeds of the neuromdulatory subcortical system (Dorsal and Medial Raphe, Ventral Tegmental Area and Nucleus Basalis of Meynert). In the same way, the relationships between the five nuclei and BrainPAD were tested. Results Results show that HD-Tyr-IDA is positively associated with LC integrity. Similarly, LC integrity disproportionally relates to better brain maintenance (BrainPAD). Mediation analyses reveal that only LC, relative to the other nuclei tested, mediates the relationship between HD-Tyr-IDA I and performance in the TMT and between HD-Tyr-IDA and BrainPAD. Conclusions These findings provide the first evidence linking tyrosine intake with LC-NA system integrity and its correlation with neuropsychological performance. This study strengthens the role of diet for maintaining brain and cognitive health and supports the noradrenergic theory of cognitive reserve. Within this framework, adequate tyrosine intake might increase the resilience of LC-NA system functioning, by preventing degeneration and supporting noradrenergic metabolism required for LC function and neuropsychological performance.

Research shows that stress, a common problem in dense cities, can be relieved in the natural environment. As great significance has been attached to the urban environment and public health in the society, this paper aims to study the relation and interaction between the perceived sensory dimension of urban park green space, attention restoration and state empathy. Therefore, we conducted an on-site questionnaire survey in four typical parks in Chengdu in terms of age, sex, daily stress, frequency of access in parks and other basic information of the respondents. The main part consists of perceived dimension, state empathy and attention restoration. The software SPSS24.0 is applied to the test of the validity and reliability of the PSD Scale, and then the important correlation between the perceived sensory dimensions in the parks and visitors' attention restoration is analyzed through multiple linear regression. Finally, the moderation effect of state empathy is tested by PROCESS. The findings show that (1) only seven dimensions in the PSD Scale are effective; (2) Serene and Refuge in the perceived sensory dimensions have significant effect on the restorative components of attention. (3) Except the dimensions of Rich in Species and Refuge, empathy enhanced the moderation effect in the interaction between the other five dimensions the Perceived Restorative Scale(PRS), especially in the interaction between the dimension Social and PRS. However, this study needs to be further explored to provide scientific basis and design strategy guidance for the research on the restoration potential of urban park green space in high-density urban areas.

We find that the overnight returns of Korean exchange-traded index funds (ETFs) are significantly positive, whereas the subsequent intraday returns are negative. These intraday return reversals are caused by relatively higher opening prices than the closing prices. In the Korean ETF market, where institutional investors are dominant participants, the return reversals are not explained by the attention hypothesis as in Berkman et al. [1]. Hence, we investigate whether the disagreement hypothesis can explain return reversals. Under the disagreement situations between positive and negative traders at the open, positive traders can have a positive influence on the ETF prices by increasing their investments. However, negative traders, who give up investments due to limited short selling opportunities in the ETF market, have no effects on the prices. Comparing ETF markets with KOSPI 200 Futures where there are no restrictions on short selling, we find that short selling constraints are significant factors for the return reversals. This implies that disagreement among the investors can cause return reversals even in the markets without noise traders. Using unique Korean market data, we conclude that return reversals cannot be completely explained by the attention hypothesis, and that disagreement among investors is also a significant factor for the return reversals. This study contributes to the existing literature by showing that the attention hypothesis does not explain return reversals in the ETF market completely, and suggesting the disagreement hypothesis as an alternative.

This study was to determine the effects of different irrigation, fertigation and mulching applications on the yield and quality parameters of strawberries. The study was conducted at the Bursa Uludağ University Yenişehir Ibrahim Orhan Vocational School Agricultural Research Field in 2019-2020. In the research, four different irrigation topics, three different fertigation and three different mulching topics were selected The maximum and minimum yield values of the study years were calculated as 5.05-18.70 t ha-1 and 1.20-8.7 t ha-1, respectively, from I100F100M1 and I25F50M0 treatments. As a result, a three-factor study determined that irrigation, fertigation, and mulching had a significant effect on the yield and quality characteristics of strawberries. However, when the reductions in yield and quality losses are evaluated together, despite the reductions in irrigation water and fertigation levels, I75 and F75 topics can be recommended. Also, in mulching treatments, black mulch material (M1) should be chosen over clear mulch material (M1) and no mulch (M0).

Poor understanding of malnutrition burden is a common reason for not prioritizing the care of small and nutritionally at-risk infants aged under-six months (infants u6m). We aimed to estimate the anthropometric deficit prevalence in infants u6m attending health centres, using the Composite Index of Anthropometric Failure (CIAF); and to assess the overlap of different individual indicators. We undertook a two-week survey of all infants u6m visiting each of 18 health centres in two zones of the Oromia region, Ethiopia. We measured weight, length, and MUAC (Mid upper arm circumference); and calculated weight-for-length (WLZ), length-for-age (LAZ), and weight-for-age z-scores (WAZ). Overall, 21.7% (95% CI: 19.2; 24.3) of infants u6m presented CIAF and of these, 10.7% (95% CI: 8.93; 12.7) had multiple anthropometric deficits. Low MUAC overlapped with 47.5% (95% CI: 38.0; 57.3), 43.8% (95% CI: 34.9; 53.1), and 42.6% (95% CI: 36.3; 49.2) of the stunted, wasted and CIAF prevalence, respectively. Underweight overlapped with 63.4% (95% CI: 53.6; 72.2), 52.7% (95% CI: 43.4; 61.7), and 59.6% (95% CI: 53.1; 65.9) of the stunted, wasted and CIAF prevalence, respectively. Anthropometric deficits, single and multiple, are prevalent in infants attending health centres. WAZ overlaps more with other forms of anthropometric deficits than MUAC.

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.

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.