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

Attention deficit hyperactivity disorder (ADHD) is a neurodevelopmental disorder characterized by a persistent pattern of inattention and/or hyperactivity-impulsivity. Neurobiologically, ADHD impairments arise from abnormalities in different circuits involving the prefrontal cortex. In face of high rates of diagnosis, alternative/complementary pharmacological therapeutic approaches for ADHD are needed. Although the number of publications that study the potential effects of caf-feine consumption on ADHD treatment have been accumulating over the last years, and caffeine has recently been used in ADHD research in the context of animal models, an updated evi-dence-based systematic review on the effects of caffeine on ADHD-like symptoms in animal stud-ies is missing. To provide insight and value at the preclinical level, a systematic review based on PRISMA guidelines was performed for all publications available up to September 1, 2021. Caffeine treatment increases attention, improves learning, memory and olfactory discrimination, without altering blood pressure and body weight. These results are supported at the neuronal level. Nonetheless, the implication of caffeine in modulating ADHD-like symptoms of hyperactivity and impulsivity is contradictory, raising discrepancies that require further clarification. Our results strengthen the hypothesis that caffeine cognitive effects found in animal models could be trans-lated to human ADHD, particularly during adolescence.

Background. ADHD is a neurodevelopmental disorder that is accompanied by executive deficits. Objectives. To obtain evidence of the usefulness of the BRIEF-P and to analyse the possible ceiling and floor effect of its scores in the assessment of executive function in preschoolers with signs compatible with a possible diagnosis of ADHD. Methods. Seven articles were selected from 2538 potentially eligible articles from different databases. We included studies assessing samples of individuals with symptoms compatible with ADHD, with age range 2-6 years, published in English or Spanish, during the period 2012-2022. References published in: Science Direct, NCBI (PubMed), ProQuest Education Journals in the last decade. Risk of bias was assessed using the QUADAS-2 questionnaire. The main variables were: age and executive functioning. Conclusions. Executive deficits in early-life individuals with ADHD-compatible symptoms are more extensive than deficits in working memory. BRIEF-P is an instrument that facilitates obtaining a sensitive and discriminative executive profile. It should be used in combination with other neuropsychological performance tests, finding a floor effect in tests associated with warm executive functions and a ceiling effect in cold executive functions. This requires using a variety of tests to assess executive performance in preschoolers with symptoms compatible with ADHD and designing intervention proposals in line with them...

This review focuses on the evidence for neurotherapeutics for Attention Deficit Hyperactivity Disorder (ADHD). EEG-Neurofeedback has been tested for about 45 years with latest meta-analyses of randomised controlled trials (RCT) showing small/medium effects compared to non-active controls only. Three small studies piloted neurofeedback of frontal activations in ADHD using functional magnetic resonance imaging or near-infrared spectroscopy, finding no superior effects over control conditions. Brain stimulation has been applied to ADHD using mostly repetitive transcranial magnetic and direct current stimulation (rTMS/tDCS). rTMS has shown mostly negative findings on improving cognition or symptoms. Meta-analyses of tDCS studies targeting mostly dorsolateral prefrontal cortex show small effects on cognitive improvements with only two out of three studies showing clinical improvements. Trigeminal nerve stimulation has shown to improve ADHD symptoms with medium effect in one RCT. Modern neurotherapeutics are attractive due to their relative safety and potential neuroplastic effects. However, they need to be thoroughly tested for clinical and cognitive efficacy across settings and beyond core symptoms and for their potential for individualised treatment.

Attention Deficit/Hyperactivity Disorder (ADHD) is a neurodevelopmental disorder that affects attention and behavioral control, characterized by constant presence of inattention and hyperactivity. This article describes a study that used a database with results from tests of voluntary and automatic attentional orientation activity in a complex network of ADHD patients, in order to understand the characteristics of the elements and attentional variability of patients. Centrality metrics and the Louvain method were applied to obtain the network communities. The most important elements of the network were identified and differences in reaction times of the activities were observed in the identified communities. The study provides useful information to understand of deficit hyperactivity disorder spectrum in patients with ADHD.

Diagnosis and treatment of Attention-Deficit/Hyperactivity Disorder (ADHD) in women remain insufficient. Fluctuations of reproductive hormones during the premenstrual period, postpartum period and (peri)menopause are neglected, even though they impact ADHD symptoms and as-sociated mood disorders [1]. Therefore, we created a female-specific treatment group for women with ADHD and premenstrual worsening of ADHD and/or mood symptoms. We describe the group programme and underlying rationale, offering a qualitative analysis of the participants’ evaluation. The 7 two-weekly sessions foreground the menstrual cycle and address several ADHD-specific topics in relation to this cyclical pattern: emotional regulation; impulsivity; setting boundaries, and triggers. Concurrently, women track their menstrual cycle and (fluctuating) ADHD and mood symptoms with an adjusted premenstrual calendar. In total, 18 women (25-47yrs) participated in three consecutive groups. We analysed the evaluation of the last group. Participants experienced the group as a safe and welcoming space. Recognition was valued by all. The topics discussed were deemed valuable and the structure suited them well. Completing the premenstrual calendar augmented awareness and recognition of individual cyclical symptoms. Discussing ADHD throughout the lifespan increased self-understanding. Participants took their menstrual cycle more seriously, prioritising self-acceptance and self-care in the luteal phase of the cycle. Exploring a cyclical approach in a group setting seems to be a positive addition to treatment for female ADHD.

Background: Virtual Reality (VR) is transforming neuropsychological assessment by providing immersive environments that closely replicate real-world conditions. This study presents the Virtual Reality Trail Making Test (TMT-VR), a novel adaptation of the traditional Trail Making Test, aimed at evaluating cognitive functions in individuals with Attention Deficit Hyperactivity Disorder (ADHD). We examined the ecological validity, convergent validity, usability, and user experience of the TMT-VR compared to the traditional version. Methods: Fifty-three participants, including 25 with ADHD and 28 neurotypical controls, completed both the traditional and VR versions of the TMT. Participants also provided feedback on the VR experience via standardized questionnaires. Results: Statistical analyses demonstrated a significant positive correlation between TMT-VR scores and both the traditional TMT and ADHD symptomatology as measured by the Adult Self-Report Scale (ASRS), confirming the TMT-VR’s ecological and convergent validity. High usability and positive user experience ratings indicated strong acceptability of the VR platform. Notably, the ADHD group exhibited greater performance differences in the VR environment, suggesting that VR may more effectively capture real-world cognitive challenges. Conclusions: These findings suggest that TMT-VR is a valid, engaging, and ecologically valid tool for cognitive assessment in ADHD, and other clinical populations, offering enhanced insights over traditional methods.

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

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

Background: The purpose of this article is to update the diagnostic assessment, therapeutic approach, and 12-18 month follow up of patients added in the Italian Lombardy ADHD Register. Methods: Data on patients evaluated by the 18 Regional ADHD Reference Centres in the ten year period from 2011 to April 2021 were analysed. Results: 4091 of 5934 added patients received a diagnosis of ADHD. In 20.3% of cases, there was a family history of ADHD. 2879 children (70.4%) had at least one comorbidity disorder, the most common of which was learning disorder (39%). Nearly all (95.9%) received at least one psychological prescription, 17.9% of them almost one pharmacological treatment, and 15.6% a combination of both. Values of ≥5 of the Clinical Global Impressions- Severity (CGI-S) are more commonly presented by patients with a pharmacological prescription than with a psychological treatment (p < .0001). A significant improvement was reported in half of the patients followed after 1 year, with Clinical Global Impressions- Improvement (CGI-I ≤ 3). In all, 233 of 4091 are 18 year old patients. Conclusion: A ten year systematic monitoring of models of care was a fruitful shared and collaborative initiative in order to promote significant improvement in clinical practice, providing effective and continuous quality of care. The unique experience here reported should spread.

Objective: This study investigated the effects of an easily implemented 8-week program centered around jump rope exercise (JRE) on executive function among children with ADHD. Methods: Using the Stroop Color Word Test to assess inhibitory control, the n-back task (1- and 2-back) to assess working memory, and task switching to assess cognitive flexibility. Results: After the intervention, there were within-group effects only for the experimental group, with higher accuracy on the 1-back working memory task (t= -2.79，p= 0.011< 0.05，Cohen’s d= 0.60) and on task switching after compared with before the JRE intervention (t= -4.00，p=0.01<0.05，Cohen’s d= 0.85), but with no change in reaction time. There was no significant within-group difference on the Stroop test (P> 0.05). Additionally, there were no between-group effects as assessed by one-way analyses of variance. Conclusion: 8-week program focused on JRE improved two aspects of executive function, working memory and cognitive flexibility, with no significant effect on a third aspect, inhibitory control, among children with ADHD aged 7 to 12 years.

ADHD, one of the most prevalent diseases during childhood, we still do not know its precisely origin, although oxidative/nitrosative stress and the hypothalamic-pituitary-adrenal axis are suggested contributors. Methylphenidate, among others, is the main drug used in these patients, but its effects on relevant markers and structures remain unclear. This study, involving 59 patients diagnosed with ADHD according to DSM-5 criteria, aimed to assess changes in cortisol levels (using cortisol awakening response, CAR) and oxidative/nitrosative status with the treatment. Blood samples before and 3 months after treatment with methylphenidate were used to measure oxidative and inflammatory markers, and the endogenous antioxidant activity, while saliva samples tracked cortisol awakening response (CAR). The results show a treatment-related improvement in the redox profile, with the reduction of advanced oxidation protein products (AOPP), lipid peroxidation (LPO), and nitrite plus nitrate (NOx) levels, and the increase of the enzymatic activities of glutathione reductase (GRd) and catalase (CAT). Moreover, the area under the curve (AUC) of CAR increased significantly, indicating increased reactivity of the HPA axis. These result support by the first time the involvement of the endogenous antioxidant system in the pathophysiology of ADHD.

The aim of this study was to examine the prevalence of self-reported and parent-reported bullying victimization, perpetration, and victimization-perpetration and the associations of autistic social impairment and attention-deficit/hyperactivity disorder (ADHD) and oppositional defiant disorder (ODD) symptoms with bullying involvement in adolescents with high functioning autism spectrum disorder (ASD). A total of 219 adolescents with high functioning ASD participated in this study. The associations of sociodemographic characteristics, parent-reported autistic social impairment on the Chinese Social Responsiveness Scale, and parent-reported ADHD and ODD symptoms on the Short form of the Swanson, Nolan, and Pelham Version IV Scale (SNAP-IV)-Chinese version with self-reported and parent-reported bullying victimization, perpetration, and victimization-perpetration evaluated using the Chinese version of the School Bullying Experience Questionnaire were examined using logistic regression analysis. The agreement between self-reported and parent-reported bullying involvement was low. Compared with bullying involvement experiences reported by adolescents themselves, parents reported higher rates of pure bullying victimization (23.7% vs. 17.8%) and victimization-perpetration (28.8% vs. 9.1%) but a lower rate of pure bullying perpetration (5.9% vs. 9.1%). Deficit in socio-communication increases the risk of being pure victims and victim-perpetrators. Parent-reported victim-perpetrators had more severe ODD symptoms than did parent-reported pure victims. The agreement between self-reported and parent-reported bullying involvement of adolescents with high functioning ASD was low. Deficit in socio-communication and ODD symptoms were significantly associated with a high risk of bullying involvement in adolescents with high functioning ASD.

Children and young people (CYP) with neurodevelopmental disorders (NDDs) may be particularly vulnerable to adverse mental health effects due to the COVID-19 pandemic. We conducted a cross-sectional U.K parent-reported study from 2nd April-2nd June 2020, using the Strengths & Difficulties Questionnaire. CYP with NDDs (n=371) compared to neurotypical controls, had a higher prevalence of emotional symptoms (42% vs 15%), conduct problems (28% vs 9%), and lower prosocial behaviours (54% vs 22%). Those with attention-deficit/hyperactivity disorder showed inflated conduct, and those with autism spectrum disorder exhibited decreased prosocial behaviours. Females with ASD had higher emotional symptoms compared to males.

Sensory processing challenges are crucial yet often neglected aspects in the care of children with neurodevelopmental disorders and genetic conditions. They represent a key area of interest in neuroscience, as they significantly impact children's daily functioning and quality of life. This observational study examines the characteristics of these challenges in a group of 614 children, aged 3 to 14 years and 11 months, divided into three groups: 183 with neurodevelopmental disorders (Autism Spectrum Disorder, Attention Deficit Hyperactivity Disorder, developmental delays, learning disorders), 89 with genetic conditions (22q11.2 Deletion Syndrome, Williams Syndrome, Pseudohypoparathyroidism), and 342 controls. Sensory processing was assessed using the Sensory Profile 2 (SP2). Results indicated that children with neurodevelopmental disorders and genetic conditions exhibited significant sensory processing difficulties compared to controls. The SP2 identified sensory challenges across different sensory systems, with varying configurations depending on the diagnosis. Children with neurodevelopmental disorders and genetic conditions show marked sensory processing challenges impacting daily lives and well-being. These findings highlight the importance of early identification and tailored evidence-based interventions to address these specific sensory processing issues. Further research should explore the long-term impact of these interventions in these different populations and their integration into broader therapeutic programs.

Brazil is the world leader in pesticide consumption, and its indiscriminate use puts farmers’ health at risk. The CYP2C9 gene stands out for encoding the CYP2C9 enzyme, which metabolizes several endogenous substrates and specific xenobiotics, especially pesticides. Our goal is to study the risk of pesticide use, espe-cially the herbicide glyphosate, in the development of diseases and the association with two CYP2C9 poly-morphisms, in farmers living in the southern region of Espírito Santo state, Brazil. The allelic frequency of CYP2C9*1, CYP2C9*2 and CYP2C9*3 was determined in blood samples from individuals exposed or not to pesticides using real-time PCR. 304 blood samples were analyzed, dividing CYP2C9 genotypes into three metabolization classes: normal, intermediate, and slow. Our results show that normal metabolizers are more vulnerable to diseases such as blood pressure changes and cardiovascular and kidney problems, while in-termediate metabolizers show an association with attention deficit disorder and miscarriages, suggesting that farmers in the studied region developed symptoms correlated to the CYP2C9 genotype they have. This work is pioneering in associating specific genetic variations and health risks with pesticide exposure, em-phasizing the importance of personalized medicine and stricter regulation of pesticide use for public health and occupational safety.

Psychiatric disorders are mental, behavioral or emotional disorders. These conditions are prevalent, one in four adults suffer from any type of psychiatric disorders world-wide. It has always been observed that psychiatric disorders have a genetic component, however new methods to sequence full genomes of large cohorts have identified with high precision genetic risk loci for these conditions. Psychiatric disorders include, but are not limited to, bipolar disorder, schizophrenia, autism spectrum disorder, anxiety disorders, major depressive disorder, and attention-deficit and hyperactivity disorder. Several risk loci for psychiatric disorders fall within genes that encode for voltage-gated calcium channels (Ca_Vs). Calcium entering through Ca_Vs is key for multiple neuronal processes. In this review, we will summarize recent findings that link Ca_Vs and their auxiliary subunits to psychiatric disorders. First, we will provide a general overview of Ca_Vs structure, classification, function, expression and pharmacology. Next, we will summarize tools and databases to study risk loci associated with psychiatric disorders. We will examine functional studies of risk variations in Ca_V genes when available. We will review pharmacological evidence of the use of Ca_V modulators to treat psychiatric disorders. Our review will be of interest for those studying pathophysiological aspects of Ca_Vs.

Attention-Deficit/Hyperactivity Disorder (ADHD) is traditionally conceptualized within a framework emphasizing deficits in attention regulation and executive function. However, emerging evidence from the fields of predictive coding and active inference offers a novel lens through which to understand ADHD not merely as a disorder of attention but as a distinctive mode of neurodevelopmental variation. This article synthesizes current research and theoretical models to propose a redefined understanding of ADHD. By integrating insights from Ryan Smith, Paul B. Badcock, and Karl J. Friston (2020), we explore the premise that individuals with ADHD exhibit unique differences in how their brains generate and update predictions about the environment, leading to the characteristic behavioral patterns observed in ADHD. We argue that ADHD involves a divergence in the brain's predictive models and its ability to minimize prediction error, impacting sensory processing, attention allocation, and motor control. This perspective illuminates the adaptive aspects of ADHD, suggesting that what are often labeled as deficits could also reflect an adaptive fit to certain environmental contexts, underscoring the role of neurodiversity in human evolution. Furthermore, we discuss the implications of this reconceptualization for clinical practice, including diagnosis, treatment, and support for individuals with ADHD, emphasizing approaches that align with their unique neurocognitive profiles. This article calls for a shift in the narrative surrounding ADHD, advocating for a model that appreciates the complexity and adaptiveness of neurodevelopmental diversity.

Videogames have been the focus of research since their widespread popularity, with particular emphasis on their effects on cognitive and affective abilities, especially in children and adolescents. Despite numerous correlational studies, robust evidence on the causal relationship between videogames and cognition remains scarce, hindered by the absence of a comprehensive assessment tool for gaming skills across various genres. This study aimed to develop and validate the Gaming Skill Questionnaire (GSQ) and to assess the impact of gaming skills in six different genres (Sport, First-Person Shooters, Role-Playing Games, Action-Adventure, Strategy, and Puzzle Games) on adolescents’ cognitive and affective abilities. The GSQ exhibited strong reliability and validity, highlighting its potential as a valuable tool. Gaming skills positively affected executive function, memory, overall cognition, cognitive flexibility, and emotion recognition, except for empathy. Various game genres had different effects on cognitive and affective abilities, with verbal fluency influenced mainly by sports, executive functions by action, strategy, and puzzle, and emotion recognition positively impacted by action and puzzle but negatively by sports and strategy games. Both age and gaming skills influenced cognitive flexibility, with gaming having a greater effect. These intriguing genre-specific effects on cognitive and affective functioning postulate further research with GSQ's contribution.

Background: Healthcare attention is sometimes considered purely technical, but communication has proven to be closely related to clinical results and patient satisfaction. Therefore, evaluation of communication in the scope of healthcare is a priority. The purpose of this study was to validate and adapt, if necessary, the Spanish version of the Communication Styles Inventory (CSI) in a sample of nursing professionals. (2) Methods: The sample was made up of 2313 nursing professionals selected at random from various medical centers in Spain, and is therefore a sample actively employed at the time data were acquired. We started out from the Communication Style Inventory, a questionnaire for evaluating the predominance of certain individual communication behaviors on six scales (expressiveness, preciseness, verbal aggressiveness, questioningness, emotionality and impression manipulativeness). (3) Results: Confirmatory Factor Analysis of the model proposed showed god fit indices. The reliability of the model shown by the Cronbach’s alpha of α=.81 was adequate, and so was single-level and aggregate consistency. Finally, in the analysis of variance by type of contract, configural, metric and scalar invariance was acceptable, but not strict invariance. (4) Conclusions: This instrument progresses in measuring non-technical attributes, such as communication styles, in nursing personnel.

This study addresses the gap in methodological guidelines for Neuroergonomic attention assessment in safety-critical tasks, focusing on validating EEG indices including the Engagement Index (EI) and Beta/Alpha ratio alongside subjective ratings. A novel task-embedded reaction time paradigm was developed to evaluate the sensitivity of these metrics to dynamic attentional demands in a more naturalistic multitasking context. By manipulating attention levels through varying secondary tasks in the NASA MATB-II task while maintaining a consistent primary reaction-time task, the study successfully demonstrated the effectiveness of the paradigm. Results indicate that both Beta/Alpha ratio and EI are sensitive to changes in attentional demands, with Beta/Alpha being more responsive to dynamic variations in attention, and EI reflecting more the overall effort required to sustain performance, especially in conditions where maintaining attention is challenging. The potential for predicting the attention lapses through integration of performance metrics, EEG measures, and subjective assessments was demonstrated, providing a more nuanced understanding of dynamic fluctuations of attention in multitasking scenarios, mimicking those in real-world safety-critical tasks. These findings provide a foundation for advancing methods to accurately monitor attention fluctuations and mitigate risks in critical scenarios, such as train-driving or automated vehicle operation, where maintaining high attention level is crucial.

The adapted Posner spatial cue-target paradigm was used to explore the relationship between exogenous temporal attention caused by rhythm and endogenous and exogenous spatial attention. The study was divided into two experiments. In Experiment 1, 33 participants used arrow cues to orient endogenous spatial attention and rhythm to orient exogenous temporal attention. In Experiment 2, with 29 participants, exogenous spatial attention was directed by highlighting the cue box. Exogenous temporal attention was directed by rhythm. Experiment 1 indicated that RTs were faster for attended targets than the unattended. The main effect of temporal attention was significant, with RTs being faster for synchrony and late compared to early. RTs were faster for late compared to synchrony. The main effect of stimulus repetition was significant, with faster RTs for targets when the rhythmic cue consisted of five compared to four. The interaction between spatial attention and temporal attention was not significant. Experiment 2 demonstrated that exogenous spatial attention only exhibited IOR under early four stimulus repetitions. The main effect of temporal attention was significant, with RTs being faster for synchrony and late compared to early. The main effect of stimulus repetition was significant, with faster RTs for targets when the rhythmic cue consisted of five compared to four. The interaction between spatial attention and temporal attention was significant. The results indicated that rhythmic temporal attention supported foreperiod effects. Exogenous temporal attention and endogenous spatial attention independently affect behavior, while exogenous temporal attention and exogenous spatial attention mutually influenced behavior.

Background: Primary deficit schizophrenia (DS) is characterized by enduring negative symptoms and represents a qualitatively different disease entity with respect to non-deficit schizophrenia (NDS). No studies investigated the association between the enzyme paraoxonase 1 (PON1) and DS and its phenomenology. Methods: In this case-control study, Thai women and men, aged 18-65 years, were divided in DS (n=40) and NDS (n=40) and were compared to controls (n=40). PON1 activities against 4-(chloromethyl)phenyl acetate (CMPA) and phenylacetate were determined. Moreover, subjects were genotyped for their PON1 Q192R polymorphism and IgA levels responses directed to Gram-negative bacteria were measured. Results: DS is significantly associated with the QQ genotype and the Q allele as compared with NDS and controls. PON1 activities are significantly and inversely associated with negative symptoms, formal thought disorders, psychomotor retardation, excitation and DS. The presence of the Q allele is associated with increased IgA responses to Pseudomonas aeruginosa, Morganella morganii, and Pseudomonas putida as compared with RR carriers. Conclusions: The PON1 Q allele and lower PON1 activities especially against CMPA are associated with DS, indicating lowered quorum quenching abilities as well as lowered defenses against lipoperoxidation and immune activation. It is suggested that lowered PON1 activity in DS constitutes an impairment in the innate immune system which together with lowered natural IgM may cause lower immune regulation thereby predisposing towards greater neurotoxic effects of immune-inflammatory, oxidative and nitrosative pathways and Gram-negative microbiota.

The Trail Making Test (TMT) is one of the most commonly administered tests in clinical and research neuropsychological settings. The two parts of the test [part A (TMT-A) and part B (TMT-B)] enable the evaluation of visuoperceptual tracking and processing speed (TMT-A), as well as divided attention, set-shifting and cognitive flexibility (TMT-B). The main cognitive processes that are assessed using TMT, i.e. processing speed, divided attention and cognitive flexibility, are often affected in patients with stroke. Considering the wide use of TMT in research and clinical settings since its introduction in neuropsychological practice, the purpose of our review was to provide a comprehensive overview on the use of TMT in stroke patients in an attempt to mainly identify its role for the evaluation of post-stroke cognitive dysfunction and progression over time, the identification of the underlying neuroanatomical pathology related to impaired TMT performance, and the association with other stroke outcomes, such motor function, driving ability and quality of life. Our comprehensive review underscores that the TMT stands as an invaluable asset in the stroke assessment toolkit, contributing nuanced insights into diverse cognitive, functional, and emotional dimensions. As research progresses, continued exploration of the TMT potential across these domains is encouraged, fostering a deeper comprehension of post-stroke dynamics, and enhancing patient-centered care across hospitals, rehabilitation centers, research institutions, and community health settings. Its integration into both research and clinical practice reaffirms TMT status as an indispensable instrument in stroke-related evaluations, enabling holistic insights that extend beyond traditional neurological assessments.

Background. The role of exercise programs during Substance Use Disorder (SUD) treatment is considered particularly supportive in fostering positive psychological and behavioral changes. The aim of this feasibility study was to explore the acute effects of cycling exercise in an immersive virtual reality (IVR) environment on attentional control and self-efficacy expectations of indi-viduals undergoing SUD treatment. Methods. A total of 20 individuals (mean age of 37.75 years) enrolled in a SUD treatment program participated in the study. They were instructed to complete a single session of cycling with a self-selected duration within the IVR system. Results. Following the cycling session, pre-post measures indicated a statistically significant im-provement in self-efficacy expectations and attentional control measured with the Stroop test. The results revealed that participants expressed high levels of intention for future use, inter-est/enjoyment, and positive attitudes toward the IVR exercise system. Qualitative data indicated that participants found the IVR exercise system enjoyable, expressed a strong intention to use it, and did not encounter technical difficulties or negative emotions. Conclusions: In conclusion, it seems that engaging in a brief period of self-selected exercise within a virtual environment can result in temporary activation of cognitive changes, heightened self-efficacy expectations, and a motivating approach to increasing physical activity participation among individuals with SUD, thus suggesting the feasibility of this approach.

Traditional 3D reconstruction models have consistently encountered a challenge: attaining high recall of object edges while preserving precision. In this paper, we introduce a post-processing method Multi-Head Attention Refiner (MA-R) aimed at tackling this challenge by integrating a multi-head attention mechanism within the U-Net style refiner module. The 3D reconstruction model applying this method demonstrates excellence in parsing intricate image details, resulting in significant enhancement of boundary predictions and increased recall rates. In our experiments, our method significantly enhances the reconstruction performance of Pix2Vox++ when multiple images are utilized as input. Specifically, it achieves an enhanced Intersection Over Union score of approximately 1.1% compared to the original Pix2Vox++ model when 16-view images are employed.

Individual investors often struggle to predict stock prices due to the limitations imposed by the computational capacities of personal laptop Graphics Processing Units (GPUs) when running intensive deep learning models. This study proposes solving these GPU constraints by integrating deep learning models with technical analysis methods. This integration significantly reduces analysis time and equips individual investors with the ability to identify stocks that may yield potential gains or losses in an efficient manner. Thus, a comprehensive buy and sell algorithm, compatible with average laptop GPU performance, is introduced in the study. This algorithm offers a lightweight analysis method that emphasizes factors identified by technical analysis methods, thereby providing a more accessible and efficient approach for individual investors. To evaluate the efficacy of this approach, we analyzed the performance of eight deep learning models: LSTM (4 layers), CNN, BiLSTM (4 layers), CNN Attention, BiGRU CNN BiLSTM Attention, BiLSTM Attention CNN, CNN BiLSTM Attention, and CNN Attention BiLSTM. These models were used to predict stock prices for Samsung Electronics and Celltrion Healthcare. The CNN Attention BiLSTM model displayed superior performance among these models, with the lowest validation mean absolute error value. In addition, an experiment was conducted using WandB Sweep to determine the optimal hyperparameters for four individual hybrid models. These optimal parameters were then implemented in each model to validate their back-testing rate of return. The CNN Attention BiLSTM hybrid model emerged as the highest-performing model, achieving an approximate rate of return of 5 percent. Overall, this study offers valuable insights into the performance of various deep learning and hybrid models in predicting stock prices. These findings can assist individual investors in selecting appropriate models that align with their investment strategies, thereby increasing their likelihood of success in the stock market.

To efficiently use its finite resources, the visual system selects for further processing only a subset of the rich sensory information. Visual masking and spatial attention control the information transfer from visual sensory-memory to visual short-term memory. There is still a debate whether these two processes operate independently or interact, with empirical evidence supporting both arguments. However, recent studies pointed out that earlier studies showing significant interactions between common-onset masking and attention suffered from ceiling and/or floor effects. Our review of previous studies reporting metacontrast-attention interactions revealed similar artifacts. Therefore, we investigated metacontrast-attention interactions by using an experimental paradigm in which ceiling/floor effects were avoided. We also examined whether metacontrast masking is differently influenced by endogenous and exogenous attention. We analyzed mean absolute-magnitude of response-errors and their statistical distribution. Our results support the hypothesis that metacontrast and endogenous/exogenous attention are largely independent with negligible likelihood for interactions. Moreover, statistical modeling of the distribution of response-errors suggests weak interactions modulating the probability of “guessing” behavior for some observers in both types of attention. Nevertheless, our data suggest that any joint effect of attention and metacontrast can be adequately explained by their independent and additive contributions.

(1) Background: Substantial evidence that neural timing deficits are prevalent in developmental disorders, aging, and concussions resulting from a mild Traumatic Brain Injury (mTBI) is presented. We show that if timing deficits are remediated using low-level movement discrimination training, then high-level cognitive skills, including reading, attention, processing speed, and working memory improve substantially. (2) Methods: Two case studies are presented using MEG source imaging on an adult dyslexic, and a healthy older adult observed before and after training on movement discrimination two times/week for 8 weeks for adult dyslexic. (3) Results: We found improvements in reading, attention, processing speed, and working memory on neuropsychological tests. Substantial MEG signal increases in visual Motion Networks (V1, V3, MT, MST), Attention Networks (ACC, dlPFC, vlPFC and precuneous/ PCC areas) and Memory Networks (dlPFC). (4) Conclusions: Improving neural timing deficits before cognitive exercises to improve specific cognitive skills provides a rapid and effective method to improve cognitive skills. Improving the timing and sensitivity of low-level dorsal pathways, improving feedforward and feedback pathways, is essential to improve high-level cognitive skills. This adaptive training with substantial feedback shows cognitive transfer to tasks not trained on, significantly improving a person’s quality of life rapidly and effectively.

In the paper the verification of forecasts of precipitation conditions measured by the standardized precipitation index SPI is presented. For the verification of categorical forecasts a contingency table was used. Standard verification measures were used for the SPI value forecast. The 30 day SPI moved every 10 days by 10 days was calculated in 2013-2015 from April to September on the basis of precipitation data from 35 meteorological stations in Poland. Predictions of the 30 day SPI were created in which precipitation was forecasted in the next 10 days (the SPI 10-day forecast) and 20 days (the SPI 20-day forecast). Both for the 10 and 20 days, the forecasts were skewed towards drier categories at the expense of wet categories. There was a good agreement between observed and 10-day forecast categories of precipitation. Less agreement is obtained for 20-day forecasts – these forecasts evidently “over-dry” the assessment of precipitation anomalies. The 10-day SPI value forecast accuracy is acceptable, whereas for the 20-day forecast is unsatisfactory. Both for the SPI categorical and the SPI value forecast, the 10-day SPI forecast is reliable and the 20-day forecast should be accepted with reservation and used with caution.

Recently, a large number of Transformer-based solutions have emerged for the trajectory prediction task, but there are shortcomings in the effectiveness of Transformers in trajectory prediction. Specifically, while position encoding preserves some of the ordering information, the self-attention mechanism at the core of the Transformer has its alignment invariance that leads to the loss of temporal information, which is crucial for trajectory prediction. For this reason, we design a simple and efficient strategy for temporal information extraction and prediction of trajectory sequences using the self-attention mechanism and linear layers. Experimental results show that the strategy we designed can effectively combine the advantages of the linear layer and self-attention mechanisms, compensating for the shortcomings of the Transformer. Additionally, we conducted an empirical study to explore the effectiveness of the linear layer and sparse self-attention mechanisms in trajectory prediction.

In today’s fast paced, attention-demanding society, executive functions and attentional resources are often taxed. Individuals need ways to sustain and restore these resources. We first review the concepts of attention and restoration, as instantiated in Attention Restoration Theory (ART). ART emphasizes the role of nature in restoring attention. We then discuss essentials of experiments on the causal influences of nature. Next, we expand the concept of ART to include modern, designed environments. We propose a wider perspective termed attentional ecology, in which attention behavior is viewed within a larger system involving the human and their interactions over time with environmental demands. When the ecology is optimal, mental functioning can be a positive “flow” that is productive, sustainable for the individual, and sometimes creative.

Detecting small objects in images poses significant challenges due to their limited pixel representation and the difficulty in extracting sufficient features, often leading to missed or false detections. To address these challenges and enhance detection accuracy, this paper presents an improved small object detection algorithm, CRL-YOLOv5. The proposed approach integrates the CBAM attention mechanism into the C3 module of the backbone network, which enhances the localization accuracy of small objects. Additionally, the Receptive Field Block (RFB) module is introduced to expand the model's receptive field, thereby fully leveraging contextual information. Furthermore, the network architecture is restructured to include an additional detection layer specifically for small objects, allowing for deeper feature extraction from shallow layers. When tested on the VisDrone2019 small object dataset, CRL-YOLOv5 achieved an mAP50 of 39.2%, representing a 5.4% improvement over the original YOLOv5, effectively boosting the detection precision for small objects in images.

Abstract: Author attribution refers to identifying the author of a document by distinguishing between texts written by various authors. Since the author’s writing style is a straightforward concept of his writing habits, an author’s attribution can be used in various sections of society, including legal work, court cases, and plagiarism. This paper studies and compares a deep architecture that automatically identifies Persian writers through their writing styles. For this purpose, since there were no existing datasets suitable for author identification in Persian, the first step was to collect three different datasets: Novels, News, and Twitter. Afterward, the Word2Vec algorithm is trained on many Persian datasets and used as word embedding. To have syntactic features besides semantic ones, Part Of Speech (POS) embedding has also been used. In the next step, an attention mechanism and Long Short-Term Memory (LSTM) are coupled with residual connections. Thus, the current layer input becomes the output of all previous layers rather than the top one. The model implemented is superior in performance for all three types of data compared to earlier models in text classification and author attribution. Absolute accuracies of 90.22%, 85.10%, and 71.39% were achieved among the three Novels, News, and Tweets datasets.

In recent years, electroencephalogram (EEG) emotion recognition has emerged as a research hotspot in the field of artificial intelligence (AI). Despite advancements in EEG-based emotion recognition, room for enhancing performance persists due to EEG signal redundancy and limitations in feature extraction, leading to inefficient models and emotional information loss. To fully utilize EEG's emotional information and improve recognition accuracy while reducing computational costs, this paper proposes a Convolutional-Recurrent Hybrid Network with a dual-stream adaptive approach and an attention mechanism (CSA-SA-CRTNN). Firstly, the model utilizes a CSAM module to assign corresponding weights to EEG channels. Then, an adaptive dual-stream convolutional-recurrent network (SA-CRNN and MHSA-CRNN) is applied to extract local spatial-temporal features. After that, the extracted local features are concatenated and fed into a temporal convolutional network with a multi-head self-attention mechanism (MHSA-TCN) to capture global information. Finally, the extracted EEG information is used for emotion classification. We conducted binary and ternary classification experiments on the DEAP dataset, achieving 99.26% and 99.15% accuracy for arousal and valence in binary classification, and 97.69% and 98.05% in ternary classification, surpassing relevant algorithms. Additionally, the model's efficiency is significantly higher than other models, achieving better accuracy with lower resource consumption.

To address the problems of insufficient semantic fusion between text and questions and the lack of consideration of global semantic information encountered in machine reading comprehension models, we proposed a machine reading comprehension model called BERT_hybrid based on BERT and hybrid attention mechanism. In this model, BERT is utilized to separately map the text and questions into the feature space. Through the integration of Bi-LSTM, attention mechanism, and self-attention mechanism, the proposed model achieves comprehensive semantic fusion between text and questions. The probabilities distribution of answers is computed using Softmax. Experimental results on the public dataset DuReader demonstrate that the proposed model achieves improvements in BLEU-4 and ROUGE-L scores compared to existing models. Furthermore, to validate the effectiveness of the proposed model design, we analyze the factors influencing the model’s performance.

The objective of person re-identification (ReID) tasks is to match a specific individual across different times, locations, or camera viewpoints. The prevalent issue of occlusion in real-world scenarios affects image information, rendering the affected features unreliable. The difficulty and core challenge lie in how to effectively discern and extract visual features from human images under various complex conditions, including cluttered backgrounds, diverse postures, and the presence of occlusions. Some works have employed pose estimation or human keypoint detection to construct graph-structured information to counteract the effects of occlusions. However, this approach introduces new noise due to issues such as the invisibility of keypoints. Our proposed module, in contrast, does not require the use of additional feature extractors. Our module employs multi-scale graph attention for the reweighting of feature importance. This allows features to concentrate on areas genuinely pertinent to the re-identification task, thereby enhancing the model's robustness against occlusions. To address these problems, a model that employs multi-scale graph attention to re-weight the importance of features is proposed in this study, significantly enhancing the model’s robustness against occlusions. Our experimental results demonstrate that, compared to baseline models, the method proposed herein achieves a notable improvement in mAP on occluded data sets, with increases of 0.5%, 31.5%, and 12.3% in mAP scores.

To enhance the safety of power grid operations, this study proposes a high-precision short-term photovoltaic power prediction method that integrates information from surrounding pho-tovoltaic stations and the Conv-LSTM-ATT model. In the deep learning prediction model, not only is numerical weather prediction (NWP) data from the target photovoltaic station used as input features, but also highly correlated features from nearby photovoltaic stations are incor-porated. The research begins by analyzing the correlation between irradiance and power se-quences, along with distance factors, to calculate a composite similarity index between the target and other regional photovoltaic stations. Stations with high similarity indices are then selected as data sources. Subsequently, Bayesian optimization techniques are employed to find the optimal data fusion ratios. Ultimately, using the selected data, power prediction mod-eling is conducted via the Conv-LSTM-ATT deep neural network. Experimental results con-firm the superiority of the proposed model, which demonstrates higher predictive accuracy compared to three other classical models. The data fusion strategy determined by Bayesian optimization significantly enhances prediction accuracy, reducing the root mean square error (RMSE) of the test set by 20.04%, 28.24%, and 30.94% for three weather types, respectively.

Session-based recommendations which aim to predict subsequent user-item interaction based on historical user behavior during anonymous sessions can be challenging tasks. Two main challenges need to be addressed and improved: (1) How to analyze these sessions to accurately and completely capture users’ preferences, and (2) how to identify and eliminate any interference caused by noisy behavior. Existing methods have not adequately addressed these issues since they either neglected the valuable insight that can be gained from analyzing consecutive groups of items or failed to take these noisy data in sessions seriously and handled them properly, which can jointly impede recommendation systems from capturing users’ real intentions. To address these two problems, we designed a multi-order semantic denoising (MSD) model for session-based recommendations. Specifically, we grouped items of different lengths as varying multi-order semantic units to mine the user’s primary intention from multiple dimensions. Meanwhile, a novel denoising network was designed to alleviate the interference of noisy behavior and provide a more precise session representation. The result of extensive experiments on three real-world datasets demonstrated that the proposed MSD model exhibited improved performance compared with existing state-of-the-art methods in session-based recommendation.

Mulberry leaves, when infected by pathogens, can suffer significant yield loss or even death if early disease detection and timely spraying are not performed. To enhance the detection performance of mulberry leaf diseases in natural environments and to precisely locate early small lesions, we propose a high-precision, high-efficiency disease detection algorithm named YOLOv8-RFMD. Based on improvements to You Only Look Once version 8 (YOLOv8), we first proposed the Multi Dimension Feature Attention (MDFA) module, which integrates important features at the pixel-level, spatial and channel dimensions. Building on this, we designed the RFMD Module, which consists of the Conv-BatchNomalization-SiLU (CBS) module, Receptive-Field Coordinated Attention (RFCA) Conv, and MDFA, replacing the Bottleneck in the model’s Residual block. We then employed Adown down sampling structure to reduce the model size and computational complexity. Finally, to improve the detection precision of small lesion features, we replaced the complete intersection over union (CIOU) loss function with the Normalized Wasserstein Distance (NWD) loss function. Results show that the YOLOv8-RFMD model achieves an mAP50 of 94.3% and an mAP50:95 of 67.8% on experimental data, representing increases of 2.9% and 4.3% respectively compared to the original model. The model size is reduced by 0.53 MB to just 5.45 MB, and the number of floating-point operations is reduced by 0.3 G to only 7.8 G. The improved model meets the deployment requirements of mobile embedded devices and can provide a theoretical reference for the automated spraying operations of mulberry leaves.

Public safety and intelligent surveillance systems critically depend on anomaly behavior detection for effective monitoring. In real-world pedestrian detection scenarios, prevalent challenges such as missed detections, complex background interference, and small target sizes hinder accurate anomaly identification. To address these issues, this study introduces YOLO-ABD, a lightweight method for anomaly behavior detection that integrates small target detection and channel shuffling. This method employs YOLOv8n as the baseline model, incorporating a small target detection mechanism in the Head part and utilizing GSConv convolution in the Backbone to enhance perceptual capability. Additionally, the SimAM attention mechanism is integrated to mitigate complex background interference, thereby improving target detection performance. Evaluation on the IITB-Corridor dataset demonstrated mAP50 and mAP50-95 scores of 89.3% and 60.6%, respectively. Generalization testing on the street-view-gdogo dataset further highlighted the superiority of YOLO-ABD over advanced detection algorithms, underscoring its effectiveness and generalizability. With a relatively small parameter count, YOLO-ABD presents an excellent lightweight solution for pedestrian anomaly behavior detection.

The limited availability of water resources worldwide presents obstacles, to the progress of ag-riculture. This situation calls for the implementation of water saving technologies to address these challenges effectively. The quest for technologies that conserve water in agriculture, par-ticularly within the context of cotton production, is related to the study of the impact of drought on the growth, leaf surface area, accumulation of dry matter, boll number, and productivity. The benefits of local drip irrigation have been established. Various models and strategies for opti-mizing and improving the efficiency of how we use water for irrigation purposes. In a market driven economy the effective utilization of water assetsis closely linked to the improvement of managing mineral nutrients. To enhance both the quantity and quality of production, nitrogen is the key factor element that plays a role in the process. The formation of highly efficient cotton crops by adapting the growing technology to the specific soil-climatic conditions of the area, in-cluding controlling nitrogen fertilizing and irrigation, is the primary method to achieve con-sistent harvests. The results of irrigation with water deficit and feeding plants with different fer-tilizer rates are presented. This matter is presently significant, in achieving dependable crop harvests supporting the economic growth of farms and safeguarding the well being of agricul-tural environments.

Due to the inherent limitations of a single viewpoint, reconstructing 3D human meshes 1 from a single image has long been a challenging task. While deep learning networks enable us to 2 approximate the shape of unseen sides, capturing the texture details of the non-visible side remains 3 difficult with just one image. Traditional methods utilize Generative Adversarial Networks (GANs) 4 to predict the normal maps of the non-visible side, thereby inferring detailed textures and wrinkles 5 on the model’s surface. However, we have identified challenges with existing normal prediction 6 networks when dealing with complex scenes, such as a lack of focus on local features and insufficient 7 modeling of spatial relationships.To address these challenges, we introduce EMAR—Enhanced 8 Multi-scale Attention-driven Single Image 3D Human Reconstruction. This approach incorporates 9 a novel Enhanced Multi-Scale Attention (EMSA) mechanism, which excels at capturing intricate 10 features and global relationships in complex scenes. EMSA surpasses traditional single-scale attention 11 mechanisms by adaptively adjusting the weights between features, enabling the network to more 12 effectively leverage information across various scales.Furthermore, we have improved the feature 13 fusion method to better integrate representations from different scales. This enhanced feature fusion 14 allows the network to more comprehensively understand both fine details and global structures 15 within the image. Finally, we have designed a hybrid loss function tailored to the introduced attention 16 mechanism and feature fusion method, optimizing the network’s training process and enhancing the 17 quality of reconstruction results.Our network demonstrates significant improvements in performance 18 for 3D human model reconstruction. Experimental results show that our method exhibits greater 19 robustness to challenging poses compared to traditional single-scale approaches.

Excessive nitrogen application has become a hidden danger threatening the sustainable development of agriculture. Exploring the potential genes will help to improve the nitrogen availability of wheat through molecular breeding. We therefore conducted wheat transcriptomics and proteomics researches under complete culture medium without nitrogen (N0) and complete culture medium with nitrogen (N1). The results showed that, the nitrogen deficit decreased the fresh weight, N content and net photosynthetic rate of winter wheat. 3949 and 2675 differentially expressed genes (DEGs) were identified in the shoot and root, respectively, while 164 and 129 differentially expressed proteins (DEPs) were identified in the shoot and root respectively. 9 genes in shoot and 26 genes in root showed the same trends in RNA and protein levels. 5 genes in shoot and 4 genes in root showed the opposite trends in RNA and protein levels. Pathway enrichment of wheat transcriptome and proteome under nitrogen deficit proposed 3 candidate genes related to photosynthesis in shoot, 4 candidate genes related to carbon metabolism in shoot, and 12 candidate genes related to carbon metabolism in root. Meanwhile, the chromosomal and subcellular localization of these genes were listed, which will provide reference for improving nitrogen availability through molecular breeding.

Multimodal emotion classification (MEC) involves analyzing and identifying human emotions by integrating data from multiple sources, such as audio, video, and text. This approach leverages the complementary strengths of each modality to enhance the accuracy and robustness of emotion recognition systems. However, one significant challenge is effectively integrating these diverse data sources, each with unique characteristics and levels of noise. Additionally, the scarcity of large, annotated multimodal datasets in Bangla limits the training and evaluation of models. In this work, we unveiled a pioneering multimodal Bangla dataset, MAViT-Bangla (Multimodal Audio Video Text Bangla dataset). This dataset, comprising 1002 samples across audio, video, and text modalities, is a unique resource for emotion recognition studies in the Bangla language. It features emotional categories such as anger, fear, joy, and sadness, providing a comprehensive platform for research. Additionally, we developed a framework for emotion recognition that employs a cross-modal attention mechanism among unimodal features. This mechanism fosters the interaction and fusion of features from different modalities, enhancing the model’s ability to capture nuanced emotional cues. The effectiveness of this approach was demonstrated by achieving an F1-score of 0.64, a significant improvement over unimodal methods.

Abstract: Background: Cancer and cancer treatments may affect aging processes, altering the trajectory of cognitive aging, but extant studies are limited in interval of assessment (2-5 years). We studied cognitive performance in a cohort of survivors and controls utilizing cross-sectional cognitive performance data from age 60 to 89 years as an indicator of potential aging trajectories and contrasted these trends with longitudinal data collected over two years. Methods: Female breast cancer survivors who had been diagnosed and treated at age 60 or older and were 5– to 15-year survivors (N=328) and non-cancer controls (N=158) were assessed at enrollment and at 8, 16 and 24 months with standard neuropsychological tests and comprehensive geriatric as-sessment. Results: Cross-sectional baseline analysis found the expected inverse association of age with cognition in both groups, with survivors performing lower than controls in learning and memory (LM) but not in attention, processing speed and executive function (APE). Younger survivors, i.e., those under 75 years of age, exhibited early decline in performance in both LM and APE compared to controls, with no differences between older survivors and controls, which tracked with deficit accumulation trends. Conclusion: Differences between survivors and controls were prominent in younger survivors, as was deficit accumulation, suggesting a mediating effect on cognition. Deficit accumulation may represent a modifiable risk factor in cancer survivorship that may be targeted for prevention and intervention.

Traffic sign detection plays a pivotal role in autonomous driving systems. The intricacy of the detection model necessitates high-performance hardware. Real-world traffic environments exhibit considerable variability and diversity, posing challenges for effective feature extraction by the model. Therefore, it is imperative to develop a detection model that is not only highly accurate but also lightweight. In this paper, we proposed YOLO-ADual, a novel lightweight model. Our method leverages the C3Dual and Adown lightweight modules as replacements for CPS and CBL modules in YOLOv5. The Adown module effectively mitigates feature loss during downsampling while reducing computational costs. Meanwhile, C3Dual optimizes the processing power for kernel feature extraction, enhancing computation efficiency while preserving network depth and feature extraction capability. Furthermore, the inclusion of the CBAM module enables the network to focus on salient information within the image, thus augmenting its feature representation capability. Our proposed algorithm achieves a mAP@0.5 of 70.1% while significantly reducing the number of parameters and computational requirements to 51.83% and 64.73% of the original model, respectively. Compared to various lightweight models, our approach demonstrates competitive performance in terms of both computational efficiency and accuracy.

YOLOv8, as an efficient object detection method, can swiftly and precisely identify objects within images. However, traditional algorithms encounter difficulties when detecting small targets in remote sensing images, such as missing information, background noise, and interactions among multiple objects in complex scenes, which may affect performance. To tackle these challenges, we propose an enhanced algorithm optimized for detecting small objects in remote sensing images, named HP-YOLOv8. Firstly, we design the C2f-D-Mixer (C2f-DM) module as a replacement for the original C2f module. This module integrates both local and global information, significantly improving the ability to detect features of small objects. Secondly, we introduce a feature fusion technique based on attention mechanisms, named Bi-Level Routing Attention in Gated Feature Pyramid Network (BGFPN). This technique utilizes an efficient feature aggregation network and reparameterization technology to optimize information interaction between different scale feature maps, and through the Bi-level Routing Attention (BRA) mechanism, it effectively captures critical feature information of small target objects. Finally, we propose the Smooth Mean Perpendicular Distance Intersection over Union (SMPDIoU) loss function. The method comprehensively considers the shape and size of detection boxes, enhances the model's focus on the attributes of detection boxes and provides a more accurate bounding box regression loss calculation method. To demonstrate our approach's efficacy, we conduct comprehensive experiments across the RSOD, NWPU VHR-10, and VisDrone2019 datasets. The experimental results show that the HP-YOLOv8 achieves 95.11%, 93.05%, and 53.49% in the mAP@0.5 metric, and 72.03%, 65.37%, and 38.91% in the more stringent mAP@0.5:0.95 metric, respectively.

The neural system has limited resources to interact with the environment, and this is where attention comes into play. Attention is a complex process that provides numerous benefits, but there has been a longstanding debate about the mechanisms involved in attentional orientation. Two widely proposed mechanisms are top-down and bottom-up, which relate to the organism's current behavioral goal and the salience of environmental stimuli, respectively. However, distinguishing between these mechanisms can be challenging in both behavioral and neural studies. This review introduces a new conceptualization in which both top-down and bottom-up mechanisms are viewed as aspects of the same process. This unified approach aims to prepare the organism to face stimuli and events based on its prior experiences, immediate (the current task that he/she is aware of), and innate (to be alive) demands.

With the global objectives of achieving a ‘carbon peak’ and ‘carbon neutrality’, along with the implementation of carbon reduction policies, China’s industrial structure has undergone significant adjustments, resulting in constraints on high-energy consumption and high-emission industries while promoting the rapid growth of green industries. Consequently, these changes have led to an increasingly complex power system structure and presented new challenges for electricity demand forecasting. To address this issue, this study proposes a 24-step multivariate time series short-term load forecasting algorithm model based on KNN data imputation and BiTCN bidirectional temporal convolutional networks combined with BiGRU bidirectional gated recurrent units and attention mechanism. The Kepler adaptive optimization algorithm (KOA) is employed for hyperparameter optimization to effectively enhance prediction accuracy. Furthermore, using real load data from a wind farm in Xinjiang as an example, this paper predicts the electricity load from January 1st to December 30th in 2019. Experimental results demonstrate that our comprehensive short-term load forecasting model exhibits lower prediction errors and superior performance compared to traditional methods, thus holding great value for practical applications.

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 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.

The period from 2019 to 2021 witnessed an enduring and severe drought that impacted southeast South America. Initially characterized as a meteorological drought, its influence later extended across the entire hydrological cycle. This paper explores the application of the S-TRACK method to monitor and characterize the 2019/21 drought in Southern Brazil (SB) and Southeast Brazil (SEB). Monthly precipitation data from reanalyses and satellites, alongside the Standardized Precipitation Index (SPI) for drought quantification, are utilized. Precipitation datasets, validated against rain gauge records, highlight ERA-5 and CHIRPS as having the highest correlation in both SB and SEB, with PERSIANN exhibiting a moderate correlation. The utilization of the S-TRACK method for spatial drought tracking revealed the temporal dynamics of the drought events, with three significant episodes occurring in 2020/2021. Initially, the onset of the drought was observed in the SB region, characterized by intensities ranging from moderate to extreme. Subsequently, the drought extended northward, reaching its maximum intensity at lower latitudes within the SEB region before either dissipating or experiencing a reduction in both its spatial extent and intensity. The prolonged presence of drought-affected areas in the same region over an extended period contributed significantly to the exacerbation of drought severity, particularly during the March to May trimester.

Recently, attention mechanisms have developed into an important tool for performance improvement of deep neural networks. In computer vision, attention mechanisms are generally divided into two main branches: spatial and channel attention. Both attention categories have their own advantages. The fusion of both attentions achieves higher performance, on the cost of the computational load. This paper introduces an innovative and lighter n-shifted sigmoid channel and spatial attention (CSA) module to reduce the computational cost and to improve the 3D scene relevant features selection. To validate the proposed attention module, 3D scene object detection in the deep Hough voting point sets is considered as the testing application. The proposed attention module with its piecewise n-shifted sigmoid activation function improves the network’s learning and generalization capacity which effectively predict bounding box parameters directly from 3D scenes and detect objects more accurately. This advantage is achieved by selectively attending to more relevant features of the input data. When used in the deep Hough voting point sets, the proposed attention module outperforms state-of-the-art 3D detection methods on the sizable SUNRGBD dataset. Experiments conducted showed an increase of 12.02 mean accuracy precision (mAP) when compared to the celebrated VoteNet (without attention). It also got 9.92 mAP higher compared to the MLVCNet, and 10.32 mAP higher than the Point Transformer. The proposed model not only decreases the sigmoid vanishing gradient problem but also brings out valuable features by fusing channel-wise and spatial information while improving accuracy results in 3D object detection.

Selecting road networks in cartographic generalization has consistently posed formidable challenges, driving research towards the application of intelligent models. Despite previous efforts, the accuracy and connectivity preservation in these studies, particularly when dealing with road types of similar sample sizes, still warrant improvement. To address these shortcomings, we introduce a Heterogeneous Graph Attention Network (HAN) for road selection, where feature masking method is initially utilized to assess the significance of road features. Concentrating on the most relevant features, two meta-paths are introduced within the HAN framework: one for aggregating features of the same road type within the first-order neighborhood, emphasizing local connectivity, and another extending this aggregation to the second-order neighborhood, capturing a broader spatial context. For a comprehensive evaluation, we introduce a set of metrics considering both quantitative and qualitative aspects of the road network. Compared to traditional neural networks, our HAN model demonstrates superior accuracy and F1-score, particularly for road types with comparable samples. Additionally, it enhances the overall connectivity of the selected network. In summary, our HAN-based method provides an advanced solution for road network selection, surpassing previous approaches in terms of accuracy and connectivity preservation.

High-density olive groves, despite their interesting production potential, have several limitations, also due to the high fruit load and the irrigation requirements.The study aimed to evaluate the effects of fruit load and deficit irrigation on oil yield and fruit quality, and on olive chemical composition in a high-density olive grove (cv Sikitita). Our main hypothesis was that the primary metabolism, as influenced by crop load and stress, could modify the accumulation of different phenolic classes.Different fruit loads were generated through flower thinning (66%, 50%, 33%, 0%) and two deficit irrigation treatments (-60%, -75%) were compared to the well-watered control (920 m3/ha).Thinning treatments had a limited effect on oil yield, on the other hand, deficit irrigation caused considerably less oil accumulation in the fruit on all sampling dates.Thinning 66% and deficit irrigation 75% were considered with the control for untargeted metabolomic analysis, considering three sampling dates. 233 different phenolic compounds were annotated. Multivariate HCA results indicated the impact of harvest time on the phenolic profile of olive fruits, yielding two separate clusters grouping t1 and t2 together and apart from t3. Regarding agronomic techniques, they played a differential role in the phenolic profile (supervised OPLS – DA). Fruit load mostly affected flavonoid glycosides. In contrast, the phenolic response to deficit irrigation is more heterogeneous, with phenolic acids (35%), flavonoids (25%), LMW, and other phenols (25%).

Cognitive deficit is a frequent non-motor symptom in Parkinson 's disease (PD) with an unclear pathogenesis. Recent research indicates possible involvement of insulin resistance and glutamate excitotoxicity in PD development. We investigated cognitive performance and the brain glutamate and insulin signaling in a rat model of PD induced by bilateral intrastriatal injection of 6-hydroxydopamine (6-OHDA). Cognitive functions were assessed with Passive avoidance (PA) and Morris Water Maze (MWM) tests. The expression of tyrosine hydroxylase (TH) and proteins involved in insulin (IR, pI3K, ERK) and glutamate receptor (AMPAR, NMDAR) signaling was assessed in the hippocampus (HPC), hypothalamus (HPT) and striatum (S) by immunofluorescence, Western blot and ELISA. Three months after 6-OHDA treatment, cognitive deficit was accompanied by decreased AMPAR activity and TH levels (HPC, S), while levels of the proteins involved in insulin signaling remained largely unchanged. Spearman’s rank correlation revealed a strong positive correlation for pAMPAR-PA (S), pNMDAR-pI3K (HPC) and pNMDAR-IR (all regions). Additionally, a positive correlation was found for TH-ERK and TH-pI3K, and a negative one for TH-MWM/errors and pI3K-MWM/time (S). These results suggest a possible association between brain glutamate (but not insulin) signaling dysfunction and cognitive deficit in a rat PD model, detected three months after 6-OHDA treatment.

The use of plant growth-promoting bacteria can be one option for mitigating the impact of abiotic constraints on different cropping systems in the tropical semi-arid region. The aim of this study was to evaluate growth, leaf gas exchange and yield in maize inoculated with Bacillus aryabhattai, and subjected to water and salt stress. The experiment followed a randomised block design, in a split plot arrangement, with six repetitions. The plots comprised two levels of electrical conductivity for the irrigation water (ECw): 0.3 dS m-1 and 3.0 dS m-1; the subplots consisted of three irrigation depths (ID1: 50%, ID2: 75%, and ID3: 100% of the crop evapotranspiration (ETc)); while the sub-subplots included the presence or absence of Bacillus aryabhattai inoculant. A water deficit of 50% of the ETc resulted in the principal negative effects on growth, reducing the leaf area and stem diameter. The use of Bacillus aryabhattai mitigated salt stress and promoted better leaf gas exchange by increasing the CO2 assimilation rate, stomatal conductance, and internal CO2 concentration. However, irrigation with brackish water (3.0 dS m-1) reduced the instantaneous water use efficiency of the maize. Inoculation partially reduced the effects of abiotic stress due to morpho-physiological traits, and increased yield potential under water deficit with no salt stress.

Since the beginning of the internet era, there has been an explosion of growth in structured data (such as numbers, symbols, and labels) as well as unstructured data (including images, videos, and text). Efficient and accurate mixed query of these two types of data is a key technology to achieve high-quality information retrieval, and it is also a major challenge that needs to be solved urgently in the industry. In this study, we employ an advanced Transformer model that combines strategies and fine-tuning techniques for multi-task learning. Specifically, the model is first pre-trained on a large-scale, general-purpose dataset to learn different types of data representations and basic language comprehension skills. After that, we fine-tuned the parameters of the model to better suit these specific data processing tasks for specific application scenarios, such as image annotation, video content analysis, and structured data query. At the heart of the model is the self-attention mechanism, which allows the model to automatically emphasize the important parts and ignore irrelevant information when processing the input data. In addition, we have introduced task-specific adaptation layers that are designed to add additional processing power to the original Transformer architecture, such as a semantic analysis layer for unstructured text data and a relational extraction layer for structured data. This combination of general pre-training and task-specific fine-tuning allows the model to flexibly process and integrate information from different data sources, improving processing efficiency and accuracy. Experimental results show that the model performs well in a variety of data processing tasks, significantly improves the accuracy and efficiency of information retrieval, and verifies the strong potential and adaptability of large language models in processing mixed data types.

The research of object classification and part segmentation is a hot topic in computer vision. A considerable number of studies have been carried out about deep learning on 3D point clouds. However,it is challenging to achieve effective feature learning due to sparsity of point clouds. Recently, a variety of Transformers have been adopted to improve point cloud processing and display great potential. Nevertheless, large numbers of Transformer layers tend to incur huge computational and memory costs. PointNet++ is one of the most influential neural architectures for point cloud understanding. Although the accuracy of PointNet++ has been largely surpassed by recent networks, this does not mean that PointNet++ has no potential.Thus, this paper offer two major contributions that significantly improve PointNet++ performance. Firstly, we introduce a novel contextual feature extraction (CFE) block that significantly enhances the feature extraction capabilities of PointNet++ networks. Secondly, to further enhance feature fusion, we seamlessly integrate a mutual learning (ML) block into the network architecture. By embedding these two innovative blocks within each layer of the network, we not only enrich the network's functionality but also impart it with greater robustness and adaptability. The specific experiments were conducted on the S3DIS (6-fold cross-validation) and Modelnet40 datasets with 86.5% and 92.7% accuracy, respectively, which proved that our model is comparable or even better than most existing methods for classification and segmentation tasks.

The success of near-infrared spectroscopy (NIRS) analysis hinges on the precision and robustness of the calibration model. Shallow learning (SL) algorithms like partial least squares discriminant analysis (PLS-DA) often fall short in capturing interrelationships between adjacent spectral variables, and the analysis results are easily affected by spectral noise, which dramatically limits the breadth and depth application of NIRS. Deep learning (DL) methods, with their capacity to discern intricate features from limited samples, are progressively integrated into NIRS. In this paper, two discriminant analysis problems including wheat kernels and Yali pears were used as examples, and several representative calibration models to research the robustness and effectiveness of the model. Additionally, this article proposed a near-infrared calibration model, which is based on gramian angular difference field and coordinate attention convolutional neural networks (G-CACNN). The research results show that, compared with SL, spectral preprocessing has a smaller impact on the analysis accuracy of consensus learning (CL) and DL, and the latter has the highest analysis accuracy in the modeling results of using the original spectrum. The accuracy of G-CACNN in two discrimination tasks is 98.48% and 99.39%, respectively. Finally, This research compared the performance of various models under noise to evaluate the robustness and noise resistance of the proposed method.

：Non-Euclidean data, such as social networks, and citation relationships between documents, has node information and structural information. Graph Convolutional Network(GCN) can automatically learn node features and association information between nodes. The core ideology of the graph convolutional network is to aggregate node information by using edge information, thereby generating a new node feature. In the process of updating node features, there are two core influencing factors. One is the number of neighboring nodes of the central node, the other is the contribution of the neighboring nodes to the central node. Due to the previous GCN methods not simultaneously considering the numbers and different contributions of neighboring nodes to the central node, we design the Adaptive Attention Mechanism(AAM). To further enhance the representational capability of the model, we utilize Multi-Head Graph Convolution(MHGC). Finally, we adopt cross-entropy(CE) loss function to describe the difference between the predicted results of node categories and the ground truth (GT). Combined with backpropagation, this ultimately achieves accurate node classification. Based on AAM, MHGC and CE, we contrive the novel Graph Adaptive Attention Network (GAAN). Experiments show that the classification accuracy has achieved outstanding performances on Cora, Citeseer and Pubmed datasets.

Previous evidence in support of a slowly acting (scale of 100s of seconds) and volume-transmitted component of cholinergic signaling was based largely on studies using measures of extracellular brain acetylcholine (ACh) levels which required several minutes to generate a single data point and typically employed AChEsterase inhibitors (AChEIs) to foster the measurement of ACh. Moreover, collecting such data points in correlation with relatively stable behavioral states has supported the view that extracellular ACh levels vary at a relatively slow rate. Here we argue that forebrain cholinergic signaling is exclusively phasic (milliseconds to perhaps seconds), unlikely to be volume-transmitted, and that previous neurochemical evidence and associated behavioral correlates may be re-interpreted in terms of integrated phasic cholinergic activity and specific behavioral and cognitive operations. The highly potent catalytic enzyme for ACh, AChE, limits the presence of an ambient extracellular ACh level and thus renders it unlikely that ACh influences target regions via relatively slow changes in extracellular ACh concentrations. Real-time amperometric recordings of cholinergic signaling have suggested a specific function of rapid, phasic or transient cholinergic signaling in attentional contexts. Optogenetic studies support a causal relationship between these transients on behavior. Combined electrochemical and neurophysiological recordings revealed that the powerful behavioral control by cholinergic transients involves the generation of high-frequency oscillations. Such oscillations are thought to recruit efferent circuitry to (re)activate dormant task sets. Evidence showing the impact of genetic variations of the capacity of cholinergic synapses likewise can be interpreted in terms of their impact on the ability to sustain generation of repeated phasic cholinergic signals, as opposed to effects on ambient ACh levels. Further, while notions of slowly-changing, sleep stage-associated variations in extracellular ACh levels and their functions are widely accepted, the evidence is in fact limited. An alternative hypothesis offers a role for high-frequency cholinergic transient signaling during REM sleep. By employing a theoretical framework that focuses on the phasic and causative characteristics and functions of cholinergic signaling, results from human cognitive neuroscience studies of cholinergic function may be substantially clarified and simplified. Compared to the current treatment of cholinergic deficits using AChEIs, the conceptualization of forebrain cholinergic signaling as wired, phasic, and causative predicts that drugs that either rescue transient presynaptic signaling or amplify or rescue the postsynaptic impact of phasic signals will be more efficacious in treating age- and dementia-related cognitive and cognitive-motor disorders.

Focusing on the problem of identifying and classifying aero-engine models, this paper measures the infrared spectrum data of aero-engine hot jets using a telemetry Fourier transform infrared spectrometer. Simultaneously, infrared spectral data sets with the six different types of aero-engines are created. For the purpose of classifying and identifying infrared spectral data, a CNN architecture based on the continuous wavelet transform peak seeking attention mechanism (CWT-AM-CNN) is suggested. This method calculates the peak value of middle wave band by continuous wavelet transform, and the peak data is extracted by the statistics of the wave number locations with high frequency. Attention mechanism is used for the peak data, and the attention mechanism is weighted to the feature map of the feature extraction block. The training set, vali-dation set and prediction set are divided in the ratio of 8:1:1 for the infrared spectral data sets. For three different data sets, CWT-AM-CNN proposed in this paper is compared with the classical classifier algorithm based on CO2 feature vector and the popular AE, RNN and LSTM spectral processing networks. The prediction accuracy of the proposed algorithm in the three data sets is as high as 97%, and the lightweight network structure design not only guarantees high precision, but also has a fast running speed, which can realize the rapid and high-precision classification of the infrared spectral data of the aero-engine hot jets.

In the context of global climate change, the frequency of sudden natural disasters is increasing. Assessing traffic road damage post-disaster is crucial for emergency decision-making and disaster management. Traditional ground observation methods for evaluating traffic road damage are limited by the timeliness and coverage of data updates. Relying solely on these methods does not adequately support rapid assessment and emergency management during extreme natural disasters. Social media, a major source of big data, can effectively address these limitations by providing more timely and comprehensive disaster information. Motivated by this, we utilized multi-source heterogeneous data to assess the damage to traffic roads under extreme conditions and established a new framework for evaluating traffic roads in cities prone to flood disasters caused by rainstorms. The approach involves several steps: First, extracting the surface area affected by precipitation using a threshold method constrained by confidence intervals derived from microwave remote sensing images; Second, collecting disaster information from the Sina Weibo platform, where social media information is screened and cleaned. A quantification table for road traffic loss assessment was defined, and a social media disaster information classification model combining text convolutional neural networks and attention mechanisms was proposed (TextCNN-Attention Disaster Classification); Finally, matching traffic road information on social media with basic geographic data, visualizing the classification of traffic road disaster risk levels, and completing the assessment of traffic road disaster levels based on multi-source heterogeneous data. Using the "7.20" rainstorm event in Henan Province as an example, this study categorizes the disaster impact on traffic roads into five levels: particularly severe, severe, moderate, mild, and affected, as interpreted from remote sensing images. The evaluation framework for flood disaster traffic roads based on multi-source heterogeneous data provides important data support and methodological support for enhancing disaster management capabilities and systems.

Background: There is now evidence that schizophrenia and deficit schizophrenia are neuro-immune conditions and that oxidative stress toxicity (OSTOX) may play a pathophysiological role. Aims of the study: To compare OSTOX biomarkers and antioxidant (ANTIOX) defenses in deficit versus non-deficit schizophrenia. Methods: We examined lipid hydroperoxides (LOOH), malondialdehyde (MDA), advanced oxidation protein products (AOPP), sulfhydryl (-SH) groups, paraoxonase 1 (PON1) activity and PON1 Q192R genotypes, total radical-trapping antioxidant parameter (TRAP) as well as immune biomarkers in patients with deficit (n=40) and non-deficit (n=40) schizophrenia and healthy controls (n=40). Results: Deficit schizophrenia is characterized by significantly increased levels of AOPP and lowered -SH, and PON1 activity, while no changes in the OSTOX/ANTIOX biomarkers were found in non-deficit schizophrenia. An increased OSTOX/ANTIOX ratio was significantly associated with deficit versus non-deficit schizophrenia (Odds ratio=3.15, p<0.001). Partial least squares analysis showed that 47.6% of the variance in a latent vector extracted from psychosis, excitation, hostility, mannerism, negative symptoms, psychomotor retardation, formal thought disorders, and neurocognitive test scores was explained by LOOH+AOPP, PON1 genotype + activity, CCL11, tumor necrosis factor (TNF)-α, IgA responses to neurotoxic tryptophan catabolites (TRYCATs), whereas -SH groups and IgM responses to MDA showed indirect effects mediated by OSTOX and neuro-immune biomarkers. Discussion: Our findings indicate that with increasing overall severity of schizophrenia, neuro-immune and neuro-oxidative (especially protein oxidation indicating chlorinative stress) toxicities become more prominent and together with lowered antioxidant defenses and impairments in innate immunity-associated resilience against neurotoxic processes shape a distinct nosological entity, namely deficit schizophrenia.

Schizophrenia comprises various symptom domains, including positive and negative symptoms. Machine learning showed that a) negative symptoms are significantly interrelated with PHEM (psychosis, hostility, excitation, and mannerism) symptoms, formal thought disorders (FTD) and psychomotor retardation (PMR); and b) stable phase schizophrenia comprises two distinct classes, namely Major Neuro-Cognitive Psychosis (MNP, largely overlapping with deficit schizophrenia) and Simple NP (SNP). In this study, we recruited 120 MNP patients and 54 healthy subjects and measured the above-mentioned symptom domains. In MNP, there were significant associations between negative and PHEM symptoms, FTD and PMR. A single latent trait, which is essentially unidimensional, underlies these key domains of schizophrenia and MNP and additionally shows excellent internal consistency reliability, convergent validity, and predictive relevance. Confirmatory Tedrad Analysis indicates that this latent vector fits a reflective model. The lack of discriminant validity shows that PHEM and negative symptoms greatly overlap and probably measure the same construct. Soft Independent Modeling of Class Analogy (SIMCA) shows that MNP (diagnosis based on negative symptoms) is better modeled using PHEM symptoms, FTD, and PMR than negative symptoms. In conclusion, in stable phase MNP, a restricted sample of the schizophrenia population, negative and PHEM symptoms, FTD and PMR belong to one underlying latent vector reflecting overall severity of schizophrenia (OSOS). The bi-dimensional concept of “positive” and “negative” symptoms cannot be validated and, therefore, future research in stable phase schizophrenia should consider that the latent phenomenon OSOS as well as its 8 reflective manifestations are the key factors of schizophrenia phenomenology.

Current case definitions of schizophrenia (DSM-5, ICD), made through a consensus among experts, are not cross-validated and lack construct reliability validity. The aim of this paper is to explain how to use bottom-up pattern recognition approaches to construct a reliable and replicable nomothetic network reflecting the direct effects of risk resilience (RR) factors, and direct and mediated effects of both RR and adverse outcome pathways (AOPs) on the schizophrenia phenome. This study was conducted using data of 40 healthy controls and 80 patients with schizophrenia. Using partial least Squares (PLS) analysis, we found that 39.7% of the variance in the phenomenome (lowered self-reported quality of life) was explained by the unified effects of AOPs (IgA to tryptophan catabolites, LPS, and the paracellular pathway, cytokines, and oxidative stress biomarkers), the cognitome (memory and executive deficits), and symptomatome (negative symptoms, psychosis, hostility, excitation, mannerism, psychomotor retardation, formal thought disorders); 55.8% of the variance in the symptomatome was explained by a single trait extracted from AOPs and the cognitome; and 22.0% of the variance in the latter was explained by the RR (Q192R polymorphism and CMPAaase activity, natural IgM, and IgM levels to zonulin). There were significant total effects (direct + mediated) of RR and AOPs on the symptomatome and phenomenome. In the current study, we built a reliable nomothetic network that reflects the associations between RR, AOPs, and the phenome of schizophrenia and discovered new diagnostic subclasses of schizophrenia based on unified RR, AOPs, and phenome scores.

Traumatic brain injury (TBI) can result in persistent sensorimotor and cognitive deficits, which occur through a cascade of deleterious pathophysiological events over time. In this study, we investigated the hypothesis that neurodegeneration caused by TBI leads to impairments in sensorimotor function. TBI induces the activation of the caspase-3 enzyme, which triggers cell apoptosis in an in vivo model of fluid percussion injury (FPI). We analyzed caspase-3 mediated apoptosis by TUNEL staining and PARP and annexin V western blotting. We correlated the neurodegeneration with sensorimotor deficits by conducting the animal behavioral tests including grid walk, balance beam, inverted screen test, and climb test. Our study demonstrated that the excess cell death or neurodegeneration correlated with the neuronal dysfunction and sensorimotor impairments associated with TBI.

With the increasing demand for security surveillance, assisted driving, and remote sensing, target detection networks with rich environmental perception capabilities and high detection accuracy have become a research hotspot. However, detection techniques based on single-modality images face limitations in environmental adaptability, as they are easily affected by lighting conditions, as well as obstacles such as smoke, rain, and vegetation, leading to information loss and failing to meet the required accuracy. To address these issues, we propose a target detection network—IV-YOLO, which integrates features from both visible and infrared images. We built a dual-branch fusion network based on YOLOv8 (You Only Look Once v8) that combines infrared and visible images for target detection. On this foundation, we designed a Bidirectional Pyramid Feature Fusion structure (Bi-Fusion) to fully integrate complementary features from different modalities, reducing detection errors caused by feature redundancy and extracting fine-grained features of small targets through dual-modal fusion. Additionally, we developed the Shuffle-SPP structure, which combines channel and spatial attention mechanisms to enhance focus on deep features and further aggregate upsampling, extracting richer features. To improve the model’s expressive power, we designed a loss function tailored for multi-scale target detection boxes, accelerating the network’s convergence during training. Experimental results show that the proposed IV-YOLO network achieves an average precision (mAP) of 74.6% on the UAV remote sensing dataset, 75.4% on the KAIST pedestrian dataset, and 84.8% on the FLIR dataset. While ensuring detection accuracy, our model significantly reduces computational load, making it well-suited to meet real-time requirements. This architecture is expected to find broad application in fields such as autonomous driving, public safety, and others.

Real-time object detection remains a pivotal topic in the realm of computer vision. Balancing the accuracy and speed of object detectors poses a formidable challenge for both academic researchers and industry practitioners. While recent transformer-based models have showncase the prowess of the attention mechanism, delivering remarkable performance improvements over CNNs, their computational demands can hinder their effectiveness in real-time detection tasks. In this paper, we elect to use YOLOX as our robust starting point and introduce a series of effective enhancements, culminating in a new high-performance detector named YOLOAX. To further exploit the power of the attention mechanism, we devise multi-dimensional attention-based modules that activate CNNs, emphasizing the most pertinent regions and bolstering the capacity to learn the most informative image representations from feature maps. Moreover, we introduce a novel leading label assignment strategy called STA, along with a groundbreaking loss function named GEIOU Loss, to further refine our detector's performance. We provide extensive ablation studies on the COCO and PASCAL VOC 2012 detection datasets to validate our proposed methods. Remarkably, our YOLOAX is trained solely on the MS-COCO dataset from scratch, without leveraging any prior knowledge, which achieves an impressive 54.2% AP on the COCO 2017 test set while maintaining a real-time speed of 72.3 fps, surpassing YOLOX by a significant margin of 3.0% AP. Our source code and pre-trained models are openly accessible at https://github.com/KejianXu/yoloax.

It has been known for a long time that many people who stutter are immediately fluent in certain conditions, for instance, when they speak in unison with others, in sync with the clicking of a metronome, or when they hear themselves speak in an altered manner. To understand why stuttering is reduced or even eliminated in such conditions is desirable because it may help understand why stuttering occurs in normal speaking conditions. However, empirical findings in this area appear conflicting and confusing, especially with regard to the role of auditory feedback. The article gives an overview of the variety and diversity of fluency-enhancing conditions and of theories proposed to explain their effect. These theories are evaluated in the light of recent empirical findings. A new hypothesis is proposed, based on findings showing that speech processing is limited without attention to the auditory channel. It is assumed that fluency-enhancing conditions draw the speaker’s attention to the auditory channel and, thereby, improve the processing of auditory feedback and its use in speech control. Implications of this account for a causal theory of stuttering and for the treatment of the disorder are discussed.

Early childhood is important for future cognitive and educational outcomes. Programs overcoming barriers to engagement in early education for Indigenous children must address family cultural needs and target developmental delays. This systematic review identifies culturally adapted programs to improve cognitive, emotional and developmental delays among young children, in response to an identified priority of a remote Indigenous community. Five databases (the Cochrane Library, Embase, Medline, Scopus and CINAHL) were searched for English language papers in January 2018. Study quality was assessed and findings analysed thematically. Findings were presented to the community at an event with key stakeholders, to determine their inclusion and face validity. Seven relevant studies, published between 1997-2013, were identified by the researchers and each study was supported by the community for inclusion. Two studies focused on Native American children and five studies included children from non-Indigenous disadvantaged backgrounds. Findings were reported narratively across four themes: Storytelling to improve educational outcomes; Family involvement improved development; Culturally adapted Cognitive Behavioral Therapy to reduce trauma; Rewards-based teaching to improve child attention. Limited published research on culturally adapted and safe interventions for children with cognitive, emotional and developmental delay exists but these four themes from seven studies identify useful components.

Plant growth regulators (PGRs) and deficit irrigation (DI) play a crucial strategic role in sustaining agricultural yield and reducing water stress in drought prone areas. The study was carried out in 2021-2022 to assess the impact of various PGRs (SA & NAA) & irrigation levels (60% Epan, 80 % Epan, and 100 % Epan) with DI & partial root drying (PRD) irrigation strategies. PGR application boosted fruit yields and demonstrated its significance in reducing water stress by lowering canopy temperature, maintaining higher relative water content in the leaves, modifying stomatal opening, and increasing photosynthetic rate. PGRs were used to assess the increased accumulation of total phenols, flavonoids, and improved antioxidant activity. Results indicated that exogenous use of PGRs like SA+NAA in conjunction with 80% irrigation levels increased fruit yield (31.97 and 40.38%) and water productivity (64.92 and 75.39%) for DI and PRD treatments, respectively. Thus standardizing irrigation and crop management practices including PGR application can be recommended to boost pomegranate productivity particularly in water-scarce environments.

Drought stress on soybean is a research-demanding matter for negative influence that agricultural drought brings about. This study was designated to evaluate the effect of drought stress on some gene expression in flowering and pod elongation stages in soybean. This experiment was carried out in split-plot format with RCBD design with four replicates. Drought stress as the main factor included three levels (irrigation after 50, 100, and 150 millimeters evaporation from the A-class evaporation pot) of which 50 millimeters evaporation is considered as control. The sub-factor included a factorial combination of 3 varieties (DPX, Sari and WE6) and two sampling stages (flowering and pod elongation). The gene expression analysis was carried out by using the QRT-PCR technique. According to our results, all genes have shown overexpression in drought stress despite this result was not the same for all genotypes and stress levels. Some genes have up-regulated in mediate stress (treatment 100) level (like as Gmdreb 2, Gmdreb 5, GmRD20A, GmaxACD2) and other genes up-regulated in serve stress (treatment 150) level. Between genotypes, DPX cultivar and WE6 line were better than of the sari cultivar for all genes up-regulated.

No studies have examined the immune fingerprint of major neuro-cognitive psychosis (MNP) or deficit schizophrenia using M1 macrophage cytokines in combination with chemokines such as CCL-2 and CCL-11. The present study delineated the neuro-immune fingerprint of MNP/deficit schizophrenia by analyzing plasma levels of IL-1β, sIL-1RA, TNF-α, sTNFR1, sTNFR2, CCL-2 and CCL-11 in MNP (n=120) versus healthy controls (n=54) in association with neurocognitive deficits (as assessed with the Brief Assessment of Cognition in Schizophrenia) and PHEMN (psychotic, hostility, excitation, mannerism and negative) symptoms. All immune biomarkers were significantly higher in MNP than in normal controls. MNP was best predicted by a combination of CCL-11, TNF-α, IL-1β and sIL-1RA which yielded a bootstrapped (n=2000) area under the Receiver Operating Curve of 0.985. Composite scores reflecting M1 macrophage activity and neurotoxic potential including combined effects of CCL-11 plus CCL-2 were significantly increased in MNP. Nevertheless, the effects of increased IL-1β and TNF-α in MNP were attenuated (statistically) by increased sIL-1RA and sTNFR2, two negative immune-regulatory markers. A large part of the variance in PHEM (38.4%-52.6%) and negative (65.8-7439%) symptoms was explained by combinations of immune markers whereby CCL-11 was consistently the most important. The immune markers also explained a large part of the variance in the Mini Mental State examination, list learning, digit sequencing task, category instances, controlled word association, symbol coding and Tower of London. Soft Independent Modeling of Class Analogy performed on the biomarkers showed that the inter-class distance between the models constructed around MNP and controls was 19.3 indicating a good separation. Partial Least Squares analysis showed that 72.7% of the variance in overall phenomenology was explained by the regression on IL-1β, sIL-1RA, CCL-11, TNF-α (all positively) and education (inversely). It is concluded that the combination of the above-mentioned markers defines MNP as a distinct neuro-immune disorder and that those markers in combination explain a large part of the variance in memory and executivive impairments and PHEMN symptoms.

Recently we showed that schizophrenia and, especially, deficit schizophrenia is accompanied by neurocognitive impairments as measured with different cognitive batteries. The aim of this study was to examine whether a single trait underpins aberrations in 9 key Cambridge Neuropsychological Test Automated Battery (CANTAB) probes, verbal fluency (VFT), world list memory (WLM), true recall, and the Mini Mental State Examination (MMSE). We recruited 80 patients with schizophrenia and 40 healthy controls. All patients were assessed using CANTAB tests, namely paired-association learning (PAL), rapid visual information (RVP), spatial working memory (SWM), one touch stocking (OTS), intra/extradimensional set shifting (IED), and emotional recognition test (ERT). We found that a single latent trait, which is essentially unidimensional, underlies the CANTAB tests, VFT, WLM, True Recall and MMSE. The latent trait shows excellent psychometric properties and fits a reflective model and, therefore, reflects a generalized cognitive decline (GCoDe), which is the cause of aberrations in semantic and episodic memory, recall, executive functions, strategy use, rule acquisition, visual sustained attention, attention set-shifting and emotional recognition. 40.5% of the variance in GCode was explained by CCL11, IgA to tryptophan catabolites, and increased oxidative toxicity. GCoDe explains 44.8% of the variance in a single latent trait extracted from psychosis, hostility, excitation, mannerism, negative symptoms, formal thought disorders, and psychomotor retardation and 40.9% in quality of life scores. GCoDe is significantly greater in deficit than in nondeficit schizophrenia. In conclusion, GCoDe mediates the effects of neuro-immune and neuro-oxidative toxicity on the phenome of (deficit) schizophrenia.

In recent decades, with the ever-growing scale of video data, near-duplicate videos continue to emerge. Data quality issue caused by near-duplicate videos is becoming more and more prominent, which has affected the application of normal videos. Although current studies on near-duplicate video detection can be helpful to uncover data quality issues for videos, they still lack a process of automatic merging for the video data represented by high-dimensional features, which are difficult to automatically clean the near-duplicate videos to improve data quality for video datasets. At present, there are few studies on near-duplicate video data cleaning. The existing studies have the sensitive problems of video data orderliness and clustering initial center under a condition that prior distribution is unknown, which seriously affect the accuracy of near-duplicate video data cleaning. To address the above issues, an automatic near-duplicate video data cleaning method based on a consistent feature hash ring is proposed in this paper. First, a residual network with convolutional block attention modules, a long short-term memory deep network, and an attention model are integrated to construct an RCLA deep network with the multi-headed attention mechanism to extract spatiotemporal features of video data. Then, a consistent feature hash ring is constructed, which can effectively alleviate the sensitivity of video data orderliness while providing a condition of near-duplicate video merging. To reduce the sensitivity of the initial cluster centers to results of near-duplicate video cleansing, an optimized feature distance-means clustering algorithm is constructed by utilizing a mountain peak function on a consistent feature hash ring, which can implement automatic cleaning of near-duplicate video data. Finally, experiments are conducted based on a commonly used dataset named CC_WEB_VIDEO and a coal mining video dataset. Compared with some existing works, simulation results demonstrate that the performance of the proposed method.

Using digital media has become the most popular leisure activity for children and adolescents. The effects of digital media use on the developing brain and cognitive processes of children are subject to debate. Here, we examine the effect of digital media use on attention subdomains in children aged 6 to 10 years. In total, 77 children participated in the study. Selective and divided attention as well as switching between attentional demands were quantified by the SwAD task. Parents were asked to assess the screen time of their children (TV, smartphone, laptop/PC, game console, tablet). Results show no main or interaction effects of screen time on any of the attention conditions investigated. Based on the present findings, as well as previous studies, we suggest a possible non-linear relationship between the amount of screen time and attention function. Furthermore, we emphasize the relevance of considering the socio-economic background of children and a need for longitudinal studies.

The aim of the present study was to predict the response of non-small cell lung cancer (NSCLC) patients to immune checkpoint inhibitors (ICIs) by leveraging computed tomography (CT) images using deep learning techniques. Retrospectively, 624 sequential CT images were gathered from 156 patients at Jiangsu Province Hospital, along with their clinical data. The dataset was subsequently partitioned into three groups: training (n=547), validation (n=64), and test (n=64). Moreover, an external validation cohort included 37 CT images from patients at Nanjing Pukou Peoples' Hospital, accompanied by comprehensive clinical data. An advanced Video Vision Transformer (ViViT) model incorporating global self-attention was utilized to analyse patients treated with ICIs and predict their response. The ViViT model's efficacy was evaluated using a confusion matrix and a receiver operating characteristic curve (ROC). Notably, the ViViT model demonstrated predictive prowess for ICIs response, yielding respective areas under the receiver operating characteristic curve (AUC) of 0.74 (95% CI: 0.69-0.78), 0.74 (95% CI: 0.61-0.86), 0.76 (95% CI: 0.62-0.88), and 0.69 (95% CI: 0.5-0.87) in the training, validation, test, and external validation cohorts. The present study illustrates how a deep learning model can provide a non-invasive means to predict clinical outcomes in NSCLC patients undergoing ICIs, potentially transforming personalized treatment approaches for individuals with NSCLC.

The lack of tactile experience is a significant flaw in online product evaluation and purchase, but visual information can be utilized to compensate for tactile deficits. In this study, we constructed a mechanistic model of the role of tactile perceived diagnosticity on consumer purchase intention based on visual compensation based on mental imagery theory, innovativeness theory, and personal goals framework. We collected data from consumers (n=406) in the Chinese market and tested the research hypotheses using structural equation modeling. The findings suggest that visually compensated tactile perceptual diagnosticity promotes mental imagery and sensory similarity, which in turn affects purchase intention. Theoretically, this study enriches current explanations of online haptics by explaining the mechanisms by which haptic demonstration videos influence consumers' haptic evaluations and behavioral responses, as well as the moderating role of personal goals therein; practically offers advice for retailers seeking to build or expand their tactile marketing strategies.

This review explores the complex interrelationships between emotion, attention, and behavior, drawing upon empirical evidence from psychological, neuroscientific, and clinical studies. It delves into the neural mechanisms that underlie these interactions, with a focus on key brain regions such as the prefrontal cortex and amygdala. Clinical cases involving stroke and attention-deficit/hyperactivity disorder (ADHD) are examined to illustrate the real-world implications of these cognitive interactions. The review also discusses the impact of effective emotion regulation strategies and the role of attention as a cognitive mechanism that influences both emotion and behavior. Neuroimaging studies, particularly functional MRI, are highlighted for their role in providing a deeper understanding of these intricate relationships. The review concludes by emphasizing the importance of this triadic relationship in adaptive functioning and its broader implications for mental health, interpersonal relationships, and quality of life.

The conventional theory of neutron beams interacting with many-body systems treats the beam as a classical system, i.e. with its dynamical variables appearing in the quantum dynamics of the scattering process not as operators but only as c-numbers. Moreover, neutrons are described with plane waves, i.e. the concept of neutron's (finite) coherence length is here irrelevant. The same holds for electron, atom or X-ray scattering. This simplification results in the full decoupling of the probe particle's dynamics from the quantum dynamics of the scatterer---a well-known fact also reflected in the standard formalism of time-correlation functions (see textbooks). Making contact with modern quantum theoretical approaches (e.g., quantum entanglement, 'which-path detection' versus interference, von Neumann measurement, Week Values, etc.) new observable effects may be exposed and/or predicted, for instance, a momentum-transfer deficit and an intensity deficit in neutron scattering from protons of hydrogen-containing samples. 7

This project investigates the behavior of multi-head attention in Transformer models, specifically focusing on the differences between benign and trojan models in the context of sentiment analysis. Trojan attacks cause models to perform normally on clean inputs but exhibit misclassifications when presented with inputs containing predefined triggers. We characterize attention head functions in trojan and benign models, identifying specific 'trojan' heads and analyzing their behavior.

Spatial attentional biases can be observed during the processing of linguistic material. For example, we previously reported that healthy subjects overestimate the semantic distance between word stimuli in the right vs left space. Here we explored whether attentional biases are also observed in tasks requiring evaluation of phonological distance between words in the right and left hemispace. Forty-one healthy subjects were presented with triplets of words arranged in space and were asked to indicate the side of space in which the phonological distance between the middle word and an outer word was smaller. In Experiment 1 real words and pseudowords were used, while in Experiment 2 only pseudowords and consonant strings were used. Subjects overestimated the phonological distance between the middle and outer words in the right space. These findings were specific to word stimuli. These results are consistent with the idea that semantic and phonological information may be internally mapped onto spatial representations.

Few-shot classification is challenging since the goal is to classify unlabeled samples with very few labeled samples provided. It has been shown that cross attention helps generate more discriminative features for few-shot learning. This paper extends the idea and proposes two cross attention modules, namely the cross scaled attention (CSA) and the cross aligned attention (CAA). Specifically, CSA scales different feature maps to make them better matched, and CAA adopts the principal component analysis to further align features from different images. Experiments showed that both CSA and CAA achieve consistent improvements over state-of-the-art methods on four widely used few-shot classification benchmark datasets, miniImageNet, tieredImageNet, CIFAR-FS, and CUB-200-2011, while CSA is slightly faster and CAA achieves higher accuracies.

Introduction: Neuroinflammation following lipopolysaccharide (LPS) administration induces locomotor deficit and anxiety-like behavior. In this study, minocycline was compared to memantine, the NMDA receptor antagonist, for its effects on LPS-induced locomotor deficit and anxiety-like behavior in rats. Methodology: Adult male Sprague Dawley rats were administered either two different doses of minocycline (25 or 50 mg/kg/day, i.p.) or 10 mg/kg/day of memantine (i.p.) for 14 days four days prior to LPS (5 mg/kg, i.p.) injection. The locomotor activity and anxiety-like behavior were assessed using the open field test (OFT). The phosphorylated tau protein level was measured using ELISA while the expression and density of brain-derived neurotrophic factor (BDNF) and cAMP response element-binding protein (CREB) protein in the medial prefrontal cortex (mPFC) were measured using immunohistochemistry and western blot, respectively. Results: In the mPFC, minocycline treatment reduced the locomotor deficit and anxiety-like behavior, reduced phosphorylated tau protein level, and upregulated BDNF/CREB protein expression comparable to memantine, with the higher dose of minocycline having better benefits. Conclusion: Minocycline treatment attenuated LPS-induced locomotor deficit and anxiety-like behavior in rats, possibly via a decrease in phosphorylated tau protein levels and an increase in the expression of the BDNF/CREB proteins.

Targets detection in synthetic aperture radar (SAR) remote sensing images, which is a fundamental but challenging problem in the field of satellite image analysis, plays an important role for a wide range of applications and is receiving significant attention in recent years. Besides, the ability of human visual system to detect visual saliency is extraordinarily fast and reliable. However, computational modeling of SAR image scene still remains a challenge. This paper analyzes the defects and shortcomings of traditional visual models applied to SAR images. Then a visual attention model designed for SAR images is proposed. The model draws the basic framework of classical ITTI model; selects and extracts the texture features and other features that can describe the SAR image better. We proposes a new algorithm for computing the local texture saliency of the input image, then the model constructs the corresponding saliency maps of features; Next, a new mechanism of feature fusion is adopted to replace the linear additive mechanism of classical models to obtain the overall saliency map; Finally, the gray-scale characteristics of focus of attention (FOA) in saliency map of all features are taken into account, our model choose the best saliency representation, Through the multi-scale competition strategy, the filter and threshold segmentation of the saliency maps can be used to select the salient regions accurately, thereby completing this operation for the visual saliency detection in SAR images. In the paper, several types of satellite image data, such as TerraSAR-X (TS-X), Radarsat-2, are used to evaluate the performance of visual models. The results show that our model provides superior performance compared with classical visual models. By further contrasting with the classical visual models, Our model reduce the false alarm caused by speckle noise, and its detection speed is greatly improved, and it is increased by 25% to 45%.

In Schizophrenia, pathway-genotypes may be constructed by combining interrelated immune biomarkers with changes in specific neurocognitive functions that represent aberrations in brain neuronal circuits. These constructs provide insight on the phenome of schizophrenia and show how pathway-phenotypes mediate the effects of genome X environmentome interactions on the symptomatology/phenomenology of schizophrenia. Nevertheless, there is a lack of knowledge how to construct pathway-phenotypes using Partial Least Squares (PLS) path modeling and Soft Independent Modeling of Class Analogy (SIMCA). This paper aims to provide a step-by-step utilization guide for the construction of pathway-phenotypes that reflect aberrations in the neuroimmune - brain circuit axis (NIBCA) in deficit schizophrenia. This NIBCA index is constructed using immune biomarkers (CCL-2, CCL-11, IL-1β, sIL-1RA, TNF-α, sTNFR1, sTNFR2) and neurocognitive tests (Brief Assessment of Cognition in Schizophrenia) predicting overall severity of schizophrenia (OSOS) in 120 deficit SCZ and 54 healthy participants. Using SmartPLS path analysis, a latent vector is extracted from those biomarkers and cognitive tests, which shows a good construct reliability (Cronbach alpha and composite reliability) and replicability and which is reflectively measured through its NIBCA manifestations. This NIBCA pathway-phenotype explains 75.0% of the variance in PHEMN (psychotic, hostility, excitation, mannerism and negative) symptoms. Using SIMCA, we constructed a NIBCA pathway-class that defines deficit schizophrenia as a qualitatively distinct nosological entity and which allows patients with deficit schizophrenia to be authenticated as belonging to the deficit schizophrenia class. In conclusion, our nomothetic approach to develop a nomological network combining neuro-immune and neurocognitive phenome markers to predict OSOS and cross-validate a diagnostic class generated replicable models reflecting the key phenome of the illness, which may mediate the effects of genome X environmentome interactions on the final outcome phenome features, namely symptomatology and phenomenology.

Abstract: For decades, researchers have suggested the existence of a bilingual cognitive advantage, especially in tasks involving executive functions such as inhibition, shifting, and updating. Recently, an increasing number of studies have questioned whether bilingualism results in change in executive functions, highlighting conflicting data published in the literature. The present study compared the performance of third-, fourth-, and fifth-grade bilingual and monolingual children on attentional and cognitive tasks. Participants were 61 monolingual and 74 bilingual children (M = 114.6 months; SD = 8.48 months) who were tested on two versions of the Attention Network Task (ANT), with and without social stimuli, as well as tests investigating working memory, short-term memory, narrative memory, and receptive vocabulary. Data on families’ socioeconomic status and children’s reasoning abilities were also collected. The results showed that bilingualism and socioeconomic status affected attentional networks in tasks involving social stimuli. In tasks involving non-social stimuli, socioeconomic status only affected the alerting and executive conflict networks. Consistent with the literature, a positive relationship emerged between socioeconomic status and executive control in the context of social stimuli, and a negative relationship emerged between socioeconomic status and the alerting network in the context of non-social stimuli. Interestingly, neither socioeconomic status nor social attentional networks correlated with working memory. Therefore, although more investigations are required, the results suggest that differences in social contexts mainly affect attentional functions.

Recently, a breakdown of paracellular and vascular pathways and activated neuroimmune and oxidative pathways were established in (deficit) schizophrenia. The aim of the current study was to delineate a) the differences in these pathways between stable phase, first (FES) and multiple (MES) episode schizophrenia, and b) the pathways that determine the behavioral-cognitive-physical-psychosocial (BCPS)-worsening in FES/MES. This study included 21 FES and 58 FES patients and 40 healthy controls and measured indicants of serum IgA to C1q, and leaky gut, immune activation, and oxidative stress toxicity (OSTOX) biomarkers. We constructed a BCPS-worsening index by extracting a latent vector from symptomatic, neurocognitive, and quality of life data. FES patients showed significantly higher IgA to C1q, cadherin, catenin, plasmalemma vesicle-associated protein, and IgA/IgM to Gram-negative bacteria than FES patients and controls. In FES patients, the BCPS-worsening score was predicted (48.7%) by IgA to Klebsiella pneumoniae and lowered paraoxonase 1 activity. In MES patients, the BCPS-worsening score was explained (42.7%) by increased tumor necrosis factor-α, OSTOX, and number of episodes. In schizophrenia, 34.0% of the variance in the BCPS-worsening score was explained by IgA to K. pneumoniae, OSTOX, and number of episodes. Increased IgA to K. pneumoniae was the single best predictor of residual psychotic symptoms in FES and MES. This study delineated different mechanistic processes in FES, including breakdown of adherens junctions, bacterial translocation, and C1q circulating immune complexes; and FES, including immune and oxidative stress neurotoxic pathways. FES and MES comprise different staging subtypes, i.e., FES and MES with and without worsening.

The present study aims to unravel the mechanisms of salicylic acid and rehydration and the physiological benefits in pepper plants grown under water deficit. 3x4 factorial scheme adopted, with three water regimes (irrigated, dry and rehydration) and four SA concentrations, namely: 0 (control), 0.5, 1 and 1.5 mM. Evaluated leaf moisture percentage, leaf water potential, chlorophylls (a and b), carotenoids, stomatal conductance, chlorophyll a fluorescence and H2O2 concentration at different times throughout the day and water conditions (irrigated, dry and rehydration) and SA applications (without the addition of the regulator (0) and with SA addition at concentrations equal to 0.5, 1 and 1.5 mM). Exogenous SA application favored stomatal conductance responses(gs) and modified fluorescence parameters (Fm, Fo, Fm', Fo', Fv/Fm, qP, ETR and NPQ) of sweet pepper plants subjected to drought followed by rehydration. SA concentration of 0.5 mM allowed a more expressive recovery of gs indices, effective quantum yield of photosystem II, with emphasis on excess energy (E) and maximum quantum yield (FV/FM) over time in plants subjected to drought and rehydration. We discovered that the use of SA, especially in association with rehydration, by modulating the variables gs and chlorophyll a fluorescence, prevents damage to membranes and consequently mitigates water deficit in pepper plants.

Segment Anything Model 2 (SAM 2) is a state-of-the-art development by Meta AI Research, designed to address the limitations of its predecessor, SAM, particularly in the realm of video segmentation. SAM 2 employs a transformer-based architecture enhanced with streaming memory, enabling real-time processing for both images and videos. This advancement is important given the exponential growth of multimedia content and the subsequent demand for efficient video analysis. Utilizing the SA-V dataset, SAM 2 excels in handling the intricate spatio-temporal dynamics inherent in video data, ensuring accurate and efficient segmentation. Key features of SAM 2 include its ability to provide real-time segmentation with minimal user interaction, maintaining robust performance even in dynamic and cluttered visual environments. This study provides a comprehensive overview of SAM 2, detailing its architecture, functionality, and diverse applications. It further explores the model's potential in improving practical implementations across various domains, emphasizing its significance in advancing real-time video analysis.

In practical applications, the detection of objects with various sizes is a common requirement for most detectors. The feature pyramid network (FPN) is widely adopted as a framework to address this challenge. The field is witnessing an increasing number of transformer-based target detectors due to the widespread adoption of transformer technology. This paper initially examines the design flaws in FPN and transformer-based target detectors, followed by the introduction of a new transformer-based approach called Texturized Instance Guidance (TIG-DETR) to address these issues. Specifically, TIG-DETR comprises a backbone network, a new pyramidal structure known as Texture-Enhanced FPN (TE-FPN), and an enhanced DETR detector.The TE-FPN is composed of three components: a bottom-up pathway for enhancing texture information in the feature map, a lightweight attention module to address confounding effects resulting from cross-scale fusion, and a standard attention module to enhance the final output features.The improved DETR detector utilizes Shifted Window based Self-Attention to replace the multi-headed self-attention module in DETR, thereby accelerating model convergence. Moreover, it incorporates an Instance Based Advanced Guidance Module to enhance instance perception in the image by employing a pre-local self-attentive mechanism for recognizing larger instances. By employing TE-FPN instead of FPN in Faster RCNN with Resnet-50 as the backbone network, we achieve a 1.9% improvement in average accuracy. TIG-DETR achieves an average accuracy of 44.1 with Resnet-50 as the backbone network.

In recent years, researchers have paid increasing attention on hyperspectral image (HSI) classification using deep learning methods. To improve the accuracy and reduce the training samples, we propose a double-branch dual-attention mechanism network (DBDA) for HSI classification in this paper. Two branches are designed in DBDA to capture plenty of spectral and spatial features contained in HSI. Furthermore, a channel attention block and a spatial attention block are applied to these two branches respectively, which enables DBDA to refine and optimize the extracted feature maps. A series of experiments on four hyperspectral datasets show that the proposed framework has superior performance to the state-of-the-art algorithm, especially when the training samples are signally lacking.

The visual span refers to the number of letters readers can identify in a single fixation without using linguistic skills. Proponents of the visual span hypothesis postulate an influence of early visual processing on reading speed. Given the slowness of reading Arabic texts, the present work aims to study the development of the visual span and its effects on reading speed in the Arabic-speaking context. Thirty-four subjects participated in the study. The trigram task and the rapid serial visual presentation (RSVP) paradigm were used to estimate visual span size and reading speed. In line with our initial assumptions, the results showed a significant effect of grade level on reading speed (F(2,31) = 30.93, p<0.001), visual span size (F(2,31) = 20.57, p<0.001). In good alignment with previous work, our results show that visual span size could explain around 40% of the reading speed variability. Interestingly, our analyses revealed a narrowing of visual span size in our Arabic sample. The results of study 2, suggest that the poor performance in the trigram task is due to poor visual attention capacities in our Arabic readers

Rice production is important for global food security but given its large water footprint, efficient irrigation management strategies need to be developed. Expansion of rice growing area is larger than any other crop in Africa due to increasing demand for rice. Three rice irrigation management alternatives with the system of rice intensification (SRI) were field-evaluated against the conventional continuously flooded system (CF) in Tanzania. Production systems included: (1) CF (50 mm ponding depth for the entire season); (2) SRI (40 mm ponding for 3 days and no irrigation for next 5 days); (3) 80% SRI (80% of the SRI ponding); and (4) 50% SRI (50% of the SRI ponding). Experimental evaluation of the four systems was conducted for both wet and dry seasons. For the dry season, the SRI and 80% SRI produced higher yields of 9.68 tons/ha and 11.45 tons/ha and saved 26% and 35% of water, respectively compared to the CF (8.69 tons/ha). The yield advantage of the 80% SRI and SRI over the CF was less during the wet season with 6.01 tons/ha and 5.99 tons/ha of production, and water savings of 30% and 14%, respectively compared to the CF (5.64 tons/ha). The 50% SRI had lowest yield of all for both seasons, 7.48 tons/ha and 4.99 tons/ha for the dry and wet seasons, respectively. Statistically, the 80% SRI treatment outperformed all other treatments over the two seasons with an additional yield of 1.57 tons/ha and 33% (345 mm) water savings compared to the CF. Economic productivity of water (US$/ha-cm) over two seasons was highest for the 80% SRI ($20.27/ha-cm), while it was lowest for the CF ($12.89/ha-cm). Water saved by converting from the CF to the 80% SRI (1.98 million ha-cm) can support a 50% expansion in the current rice irrigated area in Tanzania. Even without irrigation expansion, the 80% SRI can increase rice production by 1.5 million tons annually while enhancing water availability for industrial and environmental uses (e.g., ecological preserves) and help achieve food security in Tanzania and the greater sub-Saharan Africa.

Skin cancer is a global health concern with increasing prevalence, necessitating effective early detection and classification systems. Transfer learning has emerged as a powerful tool in this domain, enhancing diagnostic accuracy. Attention mechanisms, which selectively focus on pertinent image features, play a pivotal role in transfer learning. While previous studies have highlighted their effectiveness, their applicability across different models and datasets remains understudied. This empirical study explores the impact of various attention mechanisms on the performance of transfer learning models for skin cancer detection and classification. Using five transfer learning models (DenseNet121, InceptionV3, MobileNet, VGG16, and Xception) and six attention mechanisms (Channel Attention, Global Context Attention, Guided Attention, Nonlocal Attention, Positional Attention, and Spatial Attention), we conducted 105 experiments across three datasets. Traditional metrics (accuracy, precision, recall, and f1-score) were employed for empirical validation. The results reveal a nuanced relationship between attention mechanisms, transfer learning models, and datasets. Overall, attention mechanisms exhibit the potential to enhance skin cancer classification. Spatial and channel attention mechanisms consistently outperform others, offering simplicity and effectiveness. Model-specific selection of attention mechanisms is crucial, with a trade-off between model complexity and performance evident. This study provides insights into developing efficient skin cancer classification models utilizing attention mechanisms.

Water resources worldwide are limited, especially in desert areas like Saudi Arabia. Therefore, the rational and sustainable management of irrigation water supply is an imperative required by the conditions of space and time. This study evaluates the irrigation system through partial root-zone drying (PRD) and regulated deficit irrigation (RDI). The effect of applying the PRD and RDI systems on the efficiency and rationalization of irrigation water consumption in a greenhouse and open field cucumber was studied in addition to using the surface (S) and subsurface (SB) drip irrigation methods on irrigation efficiency. The productivity of the RDI-S regular drip irrigation treatment was considered 100% as the control on which the results of all treatments are measured. The results showed that in terms of irrigation technique, the PRD-SB technique had the highest productivity during the winter season in greenhouse cucumbers, with a general average of 13.8 kg m-2 for all irrigation levels, while the RDI-SB technique had the highest productivity during the summer season, with a general average of 16.1 kg m-2. Regarding irrigation level results, the study showed that an irrigation level of 100% is the highest productivity in all irrigation techniques, with a general average of 14.9 kg m-2 for all irrigation techniques during the winter season and 16.4 kg m-2 during the summer. However, the increase in the irrigation level to 150% did not increase productivity. In comparison, the application of the deficit irrigation technique by reducing the level of irrigation to 50% was not accompanied by a similar decrease in productivity. The study results also showed that irrigation with the PRD-SB system was the most productive in the open field at all irrigation levels, with a general average of 7.3 kg m-2. The general average of the percentage decrease in productivity in all irrigation techniques compared to reducing the irrigation percentage to 50% and 75% was 22% and 3 %. The irrigation efficiencies were higher when applying the PRD (PRD-SB) in winter. In contrast, applying a deficit irrigation system (RDI-SB) in the summer season had the highest efficiency. It could be concluded that using PRD and RDI systems for indoor and outdoor cucumber can save irrigation water.

Despite the development of biorefinery processes, so far this area does not take into account the possibility of coupling the “conventional” composting process with the production of biochemicals. However, net carbon monoxide (CO) production has been observed during bio-waste composting. So far, O2 concentration and temperature have been identified as main variables influencing CO formation. This study aimed to investigate the CO net production during bio-waste composting under controlled laboratory conditions, depending on different aeration rates and temperatures. A series of composting processes were carried out in conditions ranging from ~psychrophilic to thermophilic (T=35, 45, 55, and 65°C) and an aeration rate of 2.7, 3.4, 4.8, and 7.8 L‧h-1. The highest concentrations of CO in each thermal variant was achieved with oxygen deficit (aeration rate 2.7 L‧h-1); additionally, CO levels increased with temperature, reaching ~300 ppm at 65°C. Production of CO in mesophilic and thermophilic conditions draws attention to the biological CO formation by microorganisms capable of producing the CODH enzyme. Further research on the CO production efficiency in these thermal ranges is necessary with the characterization of the microbial community and analysis of the ability of the identified bacteria to produce the CODH enzyme and convert CO from CO2.