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The previous industrial revolutions have profoundly changed the world we live in, bringing prosperity but also affecting workers’ well-being. Today, the advent of Big Data and Artificial Intelligence has enabled what scholars call datafication, and experts posit that these technologies will reduce inequalities and make work effortless. However, other authors have opposite positions. In the present theoretical work, modern forms of labor, increasingly relying on data analysis, are interpreted through the framework of objectification. Integrating different disciplinary perspectives, we propose that workers can be seen as raw material from which to extract information, leading to the rise of Digital Taylorism. Therefore, it is necessary to question the psychological costs of adopting these technologies in the workplace: will workers feel more like data than persons? The present paper attempts to answer this question, anticipating the consequences of a digital revolution that is changing work environments and providing insights for future research.

Glioblastoma (GBM) is a common and deadly form of brain tumor in adults. Dysregulated metabolism in GBM offers an opportunity to deploy metabolic interventions as precise therapeutic strategies. To identify the molecular drivers and the modalities by which different molecular subgroups of GBM exploit metabolic rewiring to sustain tumor progression, we interrogated the transcriptome, the metabolome, and the glycoproteome of human subgroup-specific GBM sphere-forming cells (GSC). L-fucose abundance and core fucosylation activation were elevated in mesenchymal (MES) compared with proneural GSCs; this pattern was retained in subgroup-specific xenografts and in subgroup-affiliated human patient samples. Genetic and pharmacological inhibition of core fucosylation significantly reduced tumor growth in MES GBM preclinical models. Liquid chromatography-mass spectrometry (LC-MS)–based glycoproteomic screening indicated that most MES-restricted core-fucosylated proteins are involved in therapeutically relevant GBM pathological processes, such as extracellular matrix interaction, cell adhesion, and integrin-mediated signaling. Selective L-fucose accumulation in MES GBMs was observed using preclinical minimally invasive PET, implicating this metabolite as a potential subgroup-restricted biomarker. Overall, these findings indicate that L-fucose pathway activation in MES GBM is a subgroup-specific dependency that could provide diagnostic markers and actionable therapeutic targets.

We used a large-scale data-driven approach to investigate the role of word form in accessing semantics. By using distributional semantic methods and taking advantage of an ERP lexical decision mega-study, we investigated the exact time dynamic of semantic ac-cess from printed words as driven by orthography-semantics consistency (OSC) and phonology-semantics consistency (PSC). Generalized Additive Models revealed very early and late OSC-by-PSC interactions, visible at 100 and 400 msec, respectively. This pattern suggests that, during visual word recognition: a) meaning is accessed by means of two distinct and interactive paths -the orthography-to-meaning and the orthography-to -phonology-to-meaning path-, which mutually contribute to recognition since early stages; b) the system may exploit a dual mechanism for semantic access, with early and late effects associated to a fast-coarse and a slow-fine grained semantic analysis, respec-tively. The results also highlight the high sensitivity of the visual word recognition system to arbitrary form-meaning relations.(c) 2022 Elsevier Ltd. All rights reserved.