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The Aphasia Recovery Cohort, an Open-Source Chronic Stroke Repository
Get PDFSharing neuroimaging datasets enables reproducibility, education, tool development, and new discoveries. Neuroimaging from many studies are publicly available, providing a glimpse into progressive disorders and human development. In contrast, few stroke studies are shared, and these datasets lack longitudinal sampling of functional imaging, diffusion imaging, as well as the behavioral and demographic data that encourage novel applications. This is surprising, as stroke is a leading cause of disability, and acquiring brain imaging is considered standard of care. The first release of the Aphasia Recovery Cohort includes imaging data, demographics and behavioral measures from 230 chronic stroke survivors who experienced aphasia. We also share scripts to illustrate how the imaging data can predict impairment. In conclusion, recent advances in machine learning thrive on large, diverse datasets. Clinical data sharing can contribute to improvements in automated detection of brain injury, identification of white matter hyperintensities, measures of brain health, and prognostic abilities to guide care
Validation Versus Gossip: A Fine Line Dilemma
Get PDFYears ago, a friend confronted me about something that shifted my perspective on communication. They told me I wasn’t validating them enough. Instead of listening to their frustrations, I would jump into \u27fix-it\u27 mode—offering solutions, mediating, or even defending the other person. At the time, I thought I was helping. But their frustration was clear: \u27I don’t need you to solve this. I just need someone to listen.\u27
That feedback stayed with me. It made me wonder: What does it mean to validate someone’s feelings? Is listening enough? And what happens when validation crosses the line into gossip? This is a question I still wrestle with, especially because, let’s face it—gossip, no matter how grounded or principled we are, has an irresistible pull
De Novo Drug Design Using Transformer-Based Machine Translation and Reinforcement Learning of an Adaptive Monte Carlo Tree Search
Get PDFThe discovery of novel therapeutic compounds through de novo drug design represents a critical challenge in the field of pharmaceutical research. Traditional drug discovery approaches are often resource intensive and time consuming, leading researchers to explore innovative methods that harness the power of deep learning and reinforcement learning techniques. Here, we introduce a novel drug design approach called drugAI that leverages the Encoder–Decoder Transformer architecture in tandem with Reinforcement Learning via a Monte Carlo Tree Search (RL-MCTS) to expedite the process of drug discovery while ensuring the production of valid small molecules with drug-like characteristics and strong binding affinities towards their targets. We successfully integrated the Encoder–Decoder Transformer architecture, which generates molecular structures (drugs) from scratch with the RL-MCTS, serving as a reinforcement learning framework. The RL-MCTS combines the exploitation and exploration capabilities of a Monte Carlo Tree Search with the machine translation of a transformer-based Encoder–Decoder model. This dynamic approach allows the model to iteratively refine its drug candidate generation process, ensuring that the generated molecules adhere to essential physicochemical and biological constraints and effectively bind to their targets. The results from drugAI showcase the effectiveness of the proposed approach across various benchmark datasets, demonstrating a significant improvement in both the validity and drug-likeness of the generated compounds, compared to two existing benchmark methods. Moreover, drugAI ensures that the generated molecules exhibit strong binding affinities to their respective targets. In summary, this research highlights the real-world applications of drugAI in drug discovery pipelines, potentially accelerating the identification of promising drug candidates for a wide range of diseases
Mexican Consumers\u27 Attitudes Toward Irradiated and Imported Apples
Get PDFThis study centers on analyzing Mexican consumers\u27 willingness to pay (WTP) for imported US fresh apples subjected to irradiation, contrasting it with the more prevalent postharvest chemical treatments. We collect data using a survey tool in Qualtrics designed to explore the impact of information dissemination through two distinct narrative styles: scientific and layman. The study uses a between-subjects approach and apply the propensity score matching to address potential confounding factors across respondents\u27 samples. We apply the generalized multinomial logit models in WTP space, taking into consideration respondent\u27s certainty when answering to the choice experiment questions. Our findings reveal that respondents are willing to pay less for apples treated with irradiation compared to untreated ones but more than apples treated with chemicals. The WTP for irradiation increases when respondents receive information about this technology from both the scientific and layperson narrative styles. Similar to findings in previous studies, WTP for irradiated food is affected by gender, age, income, family size, and level of education. This study contributes to the literature by identifying the key factors that strongly influence consumers\u27 decisions to opt for irradiation-treated fresh fruits. These influential factors encompass information provision, social and demographic aspects, as well as the presence of country-of-origin labels. EconLit citations: C250, D820, Q160, Q180
Identifying Critical Employability Skills for Employment Success of Autistic Individuals: A Content Analysis of Job Postings
Get PDFThis study aimed to examine the literature on the skill sets of autistic individuals and determine how these skills align with current and projected future labour market needs. Based on a literature review, researchers identified the following skill categories common to autistic individuals: visual skills, attention to detail and systemizing composite skills. Researchers then gathered aggregated data on occupations and industries from over 90 state and federal sources in the United States. Next, they collected data on the most in-demand jobs, their industries and relevant skills by analysing hundreds of millions of online job postings. The results indicate the most viable occupations aligned with each skill category. There is minimal available research using labour market data to generate special education goals and transition plans for autistic students. By providing educators and practitioners with critical information regarding viable employment pathways, all stakeholders can more effectively and equitably prepare autistic individuals for the 21st-century workforce
“Reciprocal Illumination” of Hinduism, Human Rights, and the Comparative Study of Religion: Arvind Sharma’s Contributions
Get PDFArvind Sharma has made immensely significant contributions in the fields of both comparative religion and the study of Hinduism through his methodology of “reciprocal illumination” and his prominent role in international conversations on women and religion, religion and human rights, freedom of religion, and religious tolerance and conflict. Aware of the power of religion and its negative valuation, especially post-September 11, he displays a deep commitment to fostering interreligious understanding, arguing for religion as an essential and positive partner in envisioning and actualizing human flourishing, upholding human dignity, and engaging in global ethical cooperation, and equally he demonstrates Hinduism’s potential contribution both to these endeavors and to moving the field of comparative studies beyond its Western, Christian, and colonialist origins and assumptions. This essay details these contributions and Sharma’s place as an interpreter of Hinduism for those inside and outside the tradition in our time
Compassionate Noticing and Stopping the Action: Bringing Intentionally Emergent Teaching into Leadership Education
Get PDFEmergent-based practices of leadership development (such as intentional emergence (IE), case-in-point, or group relations) rely a great deal on stopping the action in order to publicly notice group behaviors and patterns and connect what is happening authentically to conscious actions and ideas (such as course content, readings, theories, etc.). However, when a facilitator or participant practices stopping the action and calling out these behaviors, there is a danger that they will go beyond productive tension into a level that causes casualties. This article explores the foundational need for compassion and purpose when using the common tools of heat and noticing in intentionally emergent spaces