Recent studies suggest a role for the microbiota in autism spectrum disorders (ASD), potentially arising from their role in modulating the immune system and gastrointestinal (GI) function or from gut–brain interactions dependent or independent from the immune system. GI problems such as chronic constipation and/or diarrhea are common in children with ASD, and significantly worsen their behavior and their quality of life. Here we first summarize previously published data supporting that GI dysfunction is common in individuals with ASD and the role of the microbiota in ASD. Second, by comparing with other publically available microbiome datasets, we provide some evidence that the shifted microbiota can be a result of westernization and that this shift could also be framing an altered immune system. Third, we explore the possibility that gut–brain interactions could also be a direct result of microbially produced metabolites.
Critical flicker fusion thresholds (CFFTs) describe when quick amplitude modulations of a light source become undetectable as the frequency of the modulation increases and are thought to underlie a number of visual processing skills, including reading. Here, we compare the impact of two vision-training approaches, one involving contrast sensitivity training and the other directional dot-motion training, compared to an active control group trained on Sudoku. The three training paradigms were compared on their effectiveness for altering CFFT. Directional dot-motion and contrast sensitivity training resulted in significant improvement in CFFT, while the Sudoku group did not yield significant improvement. This finding indicates that dot-motion and contrast sensitivity training similarly transfer to effect changes in CFFT. The results, combined with prior research linking CFFT to high-order cognitive processes such as reading ability, and studies showing positive impact of both dot-motion and contrast sensitivity training in reading, provide a possible mechanistic link of how these different training approaches impact reading abilities.
Although autism spectrum disorder (ASD) is a serious lifelong condition, its underlying neural mechanism remains unclear. Recently, neuroimaging-based classifiers for ASD and typically developed (TD) individuals were developed to identify the abnormality of functional connections (FCs). Due to over-fitting and interferential effects of varying measurement conditions and demographic distributions, no classifiers have been strictly validated for independent cohorts. Here we overcome these difficulties by developing a novel machine-learning algorithm that identifies a small number of FCs that separates ASD versus TD. The classifier achieves high accuracy for a Japanese discovery cohort and demonstrates a remarkable degree of generalization for two independent validation cohorts in the USA and Japan. The developed ASD classifier does not distinguish individuals with major depressive disorder and attention-deficit hyperactivity disorder from their controls but moderately distinguishes patients with schizophrenia from their controls. The results leave open the viable possibility of exploring neuroimaging-based dimensions quantifying the multiple-disorder spectrum.
The SARS-CoV-2 (Covid-19) virus has had severe impacts on college students' ways of life. To examine how students were coping and perceiving the Covid-19 pandemic, a secondary analysis of an online survey across the three Arizona public universities investigated students’ knowledge about Covid-19, engagement with preventive strategies, pandemic preparedness and gauged their risk perception. Results from our analysis indicate that the students were knowledgeable about Covid-19 and were changing their habits and engaging with preventive measures. Results further suggest that students were prepared for the pandemic in terms of resources and were exhibiting high-risk perceptions. The data also revealed that students who were being cautious and engaging with preventive behaviors had a higher risk-perception than individuals who were not. As for individuals who were prepared for the pandemic in terms of supplies, their risk perception was similar to those who did not have supplies. Individuals who were prepared and capable of providing a single caretaker to tend to their sick household members and isolate them in a separate room had a higher risk perception than those who could not. These results can help describe how college students will react to a future significant event, what resources students may be in need of, and how universities can take additional steps to keep their students safe and healthy. The results from this study and recommendations will provide for a stronger and more understanding campus community during times of distress and can improve upon already established university protocols for health crises and even natural disasters.
The SARS-CoV-2 (Covid-19) virus has had severe impacts on college students' ways of life. To examine how students were coping and perceiving the Covid-19 pandemic, a secondary analysis of an online survey across the three Arizona public universities investigated students’ knowledge about Covid-19, engagement with preventive strategies, pandemic preparedness and gauged their risk-perception. Results from our analysis indicate that the students were knowledgeable about Covid-19 and were changing their habits and engaging with preventive measures. Results further suggest that students were prepared for the pandemic in terms of resources and were exhibiting high-risk perceptions. The data also revealed that students who were being cautious and engaging with preventive behaviors had a higher risk-perception than individuals who were not. As for individuals who were prepared for the pandemic in terms of supplies, their risk perception was similar to those who did not have supplies. Individuals who were prepared and capable of providing a single caretaker to tend to their sick household members and isolate them in a separate room had a higher risk perception than those who could not. These results can help describe how college students will react to a future significant event, what resources students may be in need of, and how universities can take additional steps to keep their students safe and healthy. The results from this study and recommendations will provide for a stronger and more understanding campus community during times of distress and can improve upon already established university protocols for health crises and even natural disasters.
Drosophila melanogaster has been established as a model organism for investigating the developmental gene interactions. The spatio-temporal gene expression patterns of Drosophila melanogaster can be visualized by in situ hybridization and documented as digital images. Automated and efficient tools for analyzing these expression images will provide biological insights into the gene functions, interactions, and networks. To facilitate pattern recognition and comparison, many web-based resources have been created to conduct comparative analysis based on the body part keywords and the associated images. With the fast accumulation of images from high-throughput techniques, manual inspection of images will impose a serious impediment on the pace of biological discovery. It is thus imperative to design an automated system for efficient image annotation and comparison.
Results
We present a computational framework to perform anatomical keywords annotation for Drosophila gene expression images. The spatial sparse coding approach is used to represent local patches of images in comparison with the well-known bag-of-words (BoW) method. Three pooling functions including max pooling, average pooling and Sqrt (square root of mean squared statistics) pooling are employed to transform the sparse codes to image features. Based on the constructed features, we develop both an image-level scheme and a group-level scheme to tackle the key challenges in annotating Drosophila gene expression pattern images automatically. To deal with the imbalanced data distribution inherent in image annotation tasks, the undersampling method is applied together with majority vote. Results on Drosophila embryonic expression pattern images verify the efficacy of our approach.
Conclusion
In our experiment, the three pooling functions perform comparably well in feature dimension reduction. The undersampling with majority vote is shown to be effective in tackling the problem of imbalanced data. Moreover, combining sparse coding and image-level scheme leads to consistent performance improvement in keywords annotation.