Progress in simulating human geography: Assemblage theory and the practice of multi-agent artificial intelligence modeling

Over the last few years, there has been an explosion of interest in assemblage theory among human geographers. During this same period, a growing number of scholars in the field have utilized computational methodologies to simulate the complex adaptive systems they study. However, very little attention has been paid to the connections between these two developments. This article outlines those connections and argues that more explicitly integrating assemblage theory and computer modeling can encourage a more robust philosophical understanding of both and facilitate progress in scientific research on the ways in which complex socio-material systems form and transform.

Numerosity Structures the Expression of Quantity in Lexical Numbers and Grammatical Number

Using data from the World Atlas of Language Structures and other sources, this study analyzed 905 languages for the presence of grammatical number (GN) and lexical numbers (LNs) to investigate what the distribution of these linguistic features might suggest about the relationship between language and numerosity, the perceptual system for quantity. Nearly 7% of the sample had LNs but lacked GN, and GN never occurred without LNs, implying that LNs may develop first and that GN is neither necessary nor sufficient for developing LNs, despite its role in helping children acquire number concepts when present as a feature of language. The geographic-temporal distribution of the two linguistic features additionally supported the idea that LNs may emerge prior to GN. Furthermore, the “one-two-three-many” structure of both LNs and GN, along with the failure of historic artificial intelligence modeling to converge on real-world number system solutions, suggested that numerosity may structure the expression of quantity in both linguistic domains. The role of the hand in numbers (the interaction of numerosity with cognitive processes such as finger gnosia, haptic perception, and neural reactions to tools) implies that LNs may originate in tactile engagement with material structures that may subsequently extend to nontactile domains, such as GN.

AI Modeling to Combat COVID-19 Using CT Scan Imaging Algorithms and Simulations: A Study

The coronavirus disease 2019 (COVID-19) outbreak has been designated as a worldwide pandemic by World Health Organization (WHO) and raised an international call for global health emergency. In this regard, recent advancements of technologies in the field of artificial intelligence and machine learning provide opportunities for researchers and scientists to step in this battlefield and convert the related data into a meaningful knowledge through computational-based models, for the task of containment the virus, diagnosis and providing treatment. In this study, we will provide recent developments and practical implementations of artificial intelligence modeling and machine learning algorithms proposed by researchers and practitioners during the pandemic period which suggest serious potential in compliant solutions for investigating diagnosis and decision making using computerized tomography (CT) scan imaging. We will review the modern algorithms in CT scan imaging modeling that may be used for detection, quantification, and tracking of Coronavirus and study how they can differentiate Coronavirus patients from those who do not have the disease.

Multi-layered transcriptomic analyses reveal an immunological overlap between COVID-19 and hemophagocytic lymphohistiocytosis associated with disease severity

Clinical and hyperinflammatory overlap between COVID-19 and hemophagocytic lymphohistiocytosis (HLH) has been reported. However, the underlying mechanisms are unclear. Here we show that COVID-19 and HLH have an overlap of signaling pathways and gene signatures commonly dysregulated, which were defined by investigating the transcriptomes of 1253 subjects (controls, COVID-19, and HLH patients) using microarray, bulk RNA-sequencing (RNAseq), and single-cell RNAseq (scRNAseq). COVID-19 and HLH share pathways involved in cytokine and chemokine signaling as well as neutrophil-mediated immune responses that associate with COVID-19 severity. These genes are dysregulated at protein level across several COVID-19 studies and form an interconnected network with differentially expressed plasma proteins which converge to neutrophil hyperactivation in COVID-19 patients admitted to intensive care unit. scRNAseq analysis indicated that these genes are specifically upregulated across different leukocyte populations, including lymphocyte subsets and immature neutrophils. Artificial intelligence modeling confirmed the strong association of these genes with COVID-19 severity. Thus, our work indicates putative therapeutic pathways for intervention.