More Like this

1. he Brain and Language in Autism In: Language in Autism, edited by

   Larson, C; Eigsti, IM (August 2025, Wiley Blackwell)

   Schaeffer, J; Novogrodsky, R; Perovic, A; Prévost, P; Tuller, L (Ed.)

   Alongside the behavioral features of autism, this neurodevelopmental disorder is characterized by important differences in the neural circuitry underlying language processing. Regarding brain structure, most neurotypical individuals have larger left-hemisphere volumes of brain regions that are important for language, compared to the same regions in the right hemisphere (the right half of the brain). This asymmetry is due to neural specialization of left hemisphere regions for the purpose of language functions. In contrast, the brains of autistic individuals seem to be more symmetrical, suggesting that language difficulties are associated with reduced left hemisphere specialization for language in the brain. The activity of brain regions involved in language also differs in autism. Examining brain activity reveals nuanced and important differences in the processes underlying language production and comprehension in neurotypical and autistic individuals, even when their language behavior appears similar. 

   more » « less
2. Negotiating Tensions in Collaborations and its Role in Disciplinary Engagement in Science

   Krishnan, H.; Jaber, L. J.; Schellinger, J.; Southerland, S. A. (January 2022, Annual meeting program American Educational Research Association)

   While recent reforms in science education envision engaging students in doing science as a way to learn science, less is known about how such an engagement can take hold in the classroom. In an effort to address this gap, this study examines the dynamics of students’ disciplinary engagement in small groups in a middle school science classroom. Using multimodal discourse analysis, we conducted a comparative case analysis of three groups of students to examine the dynamics of their engagement along conceptual, epistemological, social, and affective dimensions. Through this analysis, our findings highlight tensions that emerge along these dimensions and the ways in which students negotiate these tensions in ways that support or hinder disciplinary engagement. 

   more » « less
3. Gaps in Information Access in Social Networks

   https://doi.org/10.1145/3308558.3313680  

   Fish, Benjamin; Bashardoust, Ashkan; Boyd, Danah; Friedler, Sorelle; Scheidegger, Carlos; Venkatasubramanian, Suresh (January 2019, The Web Conference (WWW))

   The study of influence maximization in social networks has largely ignored disparate effects these algorithms might have on the individuals contained in the social network. Individuals may place a high value on receiving information, e.g. job openings or advertisements for loans. While well-connected individuals at the center of the network are likely to receive the information that is being distributed through the network, poorly connected individuals are systematically less likely to receive the information, producing a gap in access to the information between individuals. In this work, we study how best to spread information in a social network while minimizing this access gap. We propose to use the maximin social welfare function as an objective function, where we maximize the minimum probability of receiving the information under an intervention. We prove that in this setting this welfare function constrains the access gap whereas maximizing the expected number of nodes reached does not. We also investigate the difficulties of using the maximin, and present hardness results and analysis for standard greedy strategies. Finally, we investigate practical ways of optimizing for the maximin, and give empirical evidence that a simple greedy-based strategy works well in practice. 

   more » « less
4. Recent Advances in In Vivo Neurochemical Monitoring

   https://doi.org/10.3390/mi12020208  

   Tan, Chao; Robbins, Elaine M.; Wu, Bingchen; Cui, Xinyan Tracy (February 2021, Micromachines)

   null (Ed.)

   The brain is a complex network that accounts for only 5% of human mass but consumes 20% of our energy. Uncovering the mysteries of the brain’s functions in motion, memory, learning, behavior, and mental health remains a hot but challenging topic. Neurochemicals in the brain, such as neurotransmitters, neuromodulators, gliotransmitters, hormones, and metabolism substrates and products, play vital roles in mediating and modulating normal brain function, and their abnormal release or imbalanced concentrations can cause various diseases, such as epilepsy, Alzheimer’s disease, and Parkinson’s disease. A wide range of techniques have been used to probe the concentrations of neurochemicals under normal, stimulated, diseased, and drug-induced conditions in order to understand the neurochemistry of drug mechanisms and develop diagnostic tools or therapies. Recent advancements in detection methods, device fabrication, and new materials have resulted in the development of neurochemical sensors with improved performance. However, direct in vivo measurements require a robust sensor that is highly sensitive and selective with minimal fouling and reduced inflammatory foreign body responses. Here, we review recent advances in neurochemical sensor development for in vivo studies, with a focus on electrochemical and optical probes. Other alternative methods are also compared. We discuss in detail the in vivo challenges for these methods and provide an outlook for future directions. 

   more » « less
5. Uncertainty Quantification in Inverse Models in Hydrology

   Chatterjee, Somya Sharma; Ghosh, Rahul; Renganathan, Arvind; Li, Xiang; Chatterjee, Snigdhansu; Nieber, John; Duffy, Christopher; Kumar, Vipin (August 2023, ACM)

   In hydrology, modeling streamflow remains a challenging task due to the limited availability of basin characteristics information such as soil geology and geomorphology. These characteristics may be noisy due to measurement errors or may be missing altogether. To overcome this challenge, we propose a knowledge-guided, probabilistic inverse modeling method for recovering physical characteristics from streamflow and weather data, which are more readily available. We compare our framework with state-of-the-art inverse models for estimating river basin characteristics. We also show that these estimates offer improvement in streamflow modeling as opposed to using the original basin characteristic values. Our inverse model offers a 3% improvement in R2 for the inverse model (basin characteristic estimation) and 6% for the forward model (streamflow prediction). Our framework also offers improved explainability since it can quantify uncertainty in both the inverse and the forward model. Uncertainty quantification plays a pivotal role in improving the explainability of machine learning models by providing additional insights into the reliability and limitations of model predictions. In our analysis, we assess the quality of the uncertainty estimates. Compared to baseline uncertainty quantification methods, our framework offers a 10% improvement in the dispersion of epistemic uncertainty and a 13% improvement in coverage rate. This information can help stakeholders understand the level of uncertainty associated with the predictions and provide a more comprehensive view of the potential outcomes. 

   more » « less