Beierkelley8461

Language processing is often an area of difficulty in Autism Spectrum Disorder (ASD). Semantic processing-the ability to add meaning to a stimulus-is thought to be especially affected in ASD. However, the neurological origin of these deficits, both structurally and temporally, have yet to be discovered. To further previous behavioral findings on language differences in ASD, the present study used an implicit semantic priming paradigm and electroencephalography (EEG) to compare the level of theta coherence throughout semantic processing, between typically developing (TD) and ASD participants. Theta coherence is an indication of synchronous EEG oscillations and was of particular interest due to its previous links with semantic processing. Theta coherence was analyzed in response to semantically related or unrelated pairs of words and pictures across bilateral short, medium, and long electrode connections. We found significant results across a variety of conditions, but most notably, we observed reduced coherence for language stimuli in the ASD group at a left fronto-parietal connection from 100 to 300 ms. This replicates previous findings of underconnectivity in left fronto-parietal language networks in ASD. Critically, the early time window of this underconnectivity, from 100 to 300 ms, suggests that impaired semantic processing of language in ASD may arise during pre-semantic processing, during the initial communication between lower-level linguistic processing and higher-level semantic processing. Our results suggest that language processing functions are unique in ASD compared to TD, and that subjects with ASD might rely on a temporally different language processing loop altogether.Brain network analysis is one efficient tool in exploring human brain diseases and can differentiate the alterations from comparative networks. The alterations account for time, mental states, tasks, individuals, and so forth. Furthermore, the changes determine the segregation and integration of functional networks that lead to network reorganization (or reconfiguration) to extend the neuroplasticity of the brain. Exploring related brain networks should be of interest that may provide roadmaps for brain research and clinical diagnosis. Recent electroencephalogram (EEG) studies have revealed the secrets of the brain networks and diseases (or disorders) within and between subjects and have provided instructive and promising suggestions and methods. This review summarized the corresponding algorithms that had been used to construct functional or effective networks on the scalp and cerebral cortex. We reviewed EEG network analysis that unveils more cognitive functions and neural disorders of the human and then explored the relationship between brain science and artificial intelligence which may fuel each other to accelerate their advances, and also discussed some innovations and future challenges in the end.Native Americans are the least represented population in science fields. In recent years, undergraduate and graduate level summer research programs that aimed to increase the number of Native Americans in science have made some progress. As new programs are designed, key characteristics that address science self-efficacy and science identity and provide supports for Native American students' commitment to a scientific career should be considered. In this study, we used sequential mixed methods to investigate the potential of culturally tailored internship programs on Native American persistence in science. We analyzed surveys (n = 47) and interviews (n = 4) with Native American students to understand their perceptions of themselves in relation to science research and how summer research experiences might develop science identities. Based on regression modeling, science identity, but not science self-efficacy, predicted intent to persist in science. In turn, science self-efficacy and Native American identity predicted science identity, and this suggests cultural identity is central to Native American persistence in science. In interviews, students' comments reinforced these findings and shed light on students' reasoning about the kinds of science experiences they sought; specifically, they chose to participate in culturally tailored internships because these programs provided a sense of belonging to the scientific community that did not conflict with their cultural identities. Based on our analysis, we propose an Indigenous science internship model and recommend that agencies target funding for culturally tailored programs from high school through early-investigator levels as well as provide inclusive programmatic and mentoring guidelines.In this work we introduce a simple mathematical model, based on master equations, to describe the time evolution of the popularity of hashtags on the Twitter social network. Specifically, we model the total number of times a certain hashtag appears on user's timelines as a function of time. Our model considers two kinds of components those that are internal to the network (degree distribution) as well as external factors, such as the external popularity of the hashtag. From the master equation, we are able to obtain explicit solutions for the mean and variance and construct confidence regions. We propose a gamma kernel function to model the hashtag popularity, which is quite simple and yields reasonable results. We validate the plausibility of the model by contrasting it with actual Twitter data obtained through the public API. Our findings confirm that relatively simple semi-deterministic models are able to capture the essentials of this very complex phenomenon for a wide variety of cases. The model we present distinguishes from other existing models in its focus on the time evolution of the total number of times a particular hashtag has been seen by Twitter users and the consideration of both internal and external components.

COVID-19 constitutes an unprecedented mental health challenge to the world. At this critical time, it is important to identify factors that may boost individuals' well-being or render individuals more resistant to the negative impact of COVID-19-related stressors. The goals of this study were to examine whether individuals' and their partners' worry about COVID-19 were linked to individuals' psychological, social, and cognitive adjustment and test individuals' and their partners' mindfulness as possible moderators.

Cross-sectional, dyadic data were collected from 211 Chinese couples with kindergarten-aged children living in Hong Kong, China, during its fourth major outbreak of COVID-19 (between December 2020 and January 2021). Using paper-and-pencil questionnaires, fathers and mothers independently reported their worry about COVID-19, mindfulness, depressive symptoms, social difficulties, and cognitive problems.

Actor-Partner-Interdependence Models revealed that, controlling for individuals' gender and education levels, individuals' worry about COVID-19 and mindfulness were positively and negatively associated with their own depressive symptoms, social difficulties, and cognitive problems, respectively. The worry of individuals' partners was also positively associated with individuals' depressive symptoms and social difficulties. These associations, however, were only significant when the partners had low but not high levels of mindfulness.

Our study highlighted the importance of studying the potential benefits of mindfulness at not only the individual but also the dyadic level.

Our study highlighted the importance of studying the potential benefits of mindfulness at not only the individual but also the dyadic level.Today, there is a level of panic and chaos dominating the entire world due to the massive outbreak in the second wave of COVID-19 disease. As the disease has numerous symptoms ranging from a simple fever to the inability to breathe, which may lead to death. One of these symptoms is a cough which is considered one of the most common symptoms for COVID-19 disease. Recent research shows that the cough of a COVID-19 patient has distinct features that are different from other diseases. UNC8153 cell line Consequently, the cough sound can be detected and classified to be used as a preliminary diagnosis of the COVID-19, which will help in reducing the spreading of that disease. The artificial intelligence (AI) engine can diagnose COVID-19 diseases by executing differential analysis of its inherent characteristics and comparing it to other non-COVID-19 coughs. However, the diagnosis of a COVID-19 infection by cough alone is an extremely challenging multidisciplinary problem. Therefore, this paper proposes a hybrid framework for efficiethe most, thereby saving more lives.Backtracking search algorithm (BSA) is a nature-based optimization technique extensively used to solve various real-world global optimization problems for the past few years. The present work aims to introduce an improved BSA (ImBSA) based on a multi-population approach and modified control parameter settings to apprehend an ensemble of various mutation strategies. In the proposed ImBSA, a new mutation strategy is suggested to enhance the algorithm's performance. Also, for all mutation strategies, the control parameters are updated adaptively during the algorithm's execution. Extensive experiments have been performed on CEC2014 and CEC2017 single-objective benchmark functions, and the results are compared with several state-of-the-art algorithms, improved BSA variants, efficient differential evolution (DE) variants, particle swarm optimization (PSO) variants, and some other hybrid variants. The nonparametric Friedman rank test has been conducted to examine the efficiency of the proposed algorithm statistically. Moreover, six real-world engineering design problems have been solved to examine the problem-solving ability of ImBSA. The experimental results, statistical analysis, convergence graphs, complexity analysis, and the results of real-world applications confirm the superior performance of the suggested ImBSA.Managing our transition to sustainability requires a solid understanding of how conditions of financial crisis affect our natural environment. Yet, there has been little focus on the nature of the relationship between financial crises and environmental sustainability, especially in relation to forests and deforestation. This study addressed this gap by providing novel evidence on the impact of financial crises on deforestation. A panel data approach is used looking at Global Forest Watch deforestation data from > 150 countries in > 100 crises in the twenty-first century. This includes an analysis of crises effects on principle drivers of deforestation; timber and agricultural commodities-palm oil, soybean, coffee, cattle, and cocoa. At a global level, financial crises are associated with a reduction in deforestation rates (- 36 p.p) and deforestation drivers; roundwood (- 6.7 p.p.), cattle (- 2.3 p.p.) and cocoa production (- 8.3 p.p.). Regionally, deforestation rates in Asia, Africa, and Europe decreased by orest loss/gain data, disturbance history, and understanding of mosaicked landscape dynamics within a satellite pixel.[This corrects the article on p. 274 in vol. 11, PMID 30894882.].