julilili42/papers huggingface dataset:
Towards Rhetorical AI Literacy. Presenting a Conceptual Framework
2025-01-01

This paper is conceptual in nature: it does not propose a fixed framework for a specific learning context; instead, it builds on research in classical and digital rhetoric, competence development, and AI literacy to argue for the necessity of a distinct Rhetorical AI Literacy. To develop such a flexible rhetorical framework, the paper outlines the fundamental literacy requirements for users and examines the interplay between human and machine communication. This paper first identifies a gap in existing AI competence frameworks – their neglect of rhetoric as an essential dimension of AI literacy – and then frames its analysis around three interconnected domains: pragmatism, offering a practical lens on AI’s evolving role in discourse and decision making; interface design, which shapes user–AI interactions; and technè, highlighting the procedural skills central to rhetorical practice. Taken together, these perspectives reconceive generative AI not merely as a technical tool but as a fundamentally rhetorical system that shapes knowledge production, argumentation, and meaning making. Then, drawing on Aristotle’s conception of technè as poiesis and Isocrates’ emphasis on rhetorical situatedness as praxis, the paper develops a production-oriented framework for Rhetorical AI Literacy. Central to this approach is the question of how human rhetorical judgment can be fostered and exercised effectively in specific contexts of collaboration with generative AI. To address this, an educational scenario is proposed – one that integrates technological, rhetorical, and ethical considerations, and structures the learning and exercise of rhetorical judgment along four dimensions: 1. Co-activity and accessibility: AI as a rhetorical partner; 2. Bullshit generators and Promptology: managing AI’s rhetorical illusions; 3. Adopting and adapting: rhetorical usage practices; 4. Human responsibility and contextual accountability. In doing so, the paper aims to illuminate the paradigmatic situation of co-activity that humans experience with generative AI systems, concluding with an outlook on promising areas of future research accessible through the lens of Rhetorical AI Literacy.
Zertum and Dubium in Persuasion or the Othello Reaction
2025-01-01

This paper introduces and explains the double-z model of persuasion, which applies to the mental instances of certainty and doubt, modeled here as theoretical constructs. They stand in a dialectical relationship with each other in rhetorical events. The category of zertum is of particular importance in the core process of persuasion, which is associated with the term dogma here. A dense structural and functional analysis of a short scene from Shakespeare's Othello is followed by a theoretical discussion in which the double-z model is formalized and explained in detail. The final section concentrates on theoretical questions of pragmatics. The role of framings and communicative embeddings of the double-z structure is treated, as well as the evaluation consequences of assignments to certain types of communication (to rhetoric or manipulation).
What is Strategic in Strategic Maneuvering?
2025-01-01

Since the late 1990s, the Amsterdam school of Pragma-Dialectics has extended its theory of a critical discussion by the integration of the concept of « strategic maneuvering ». The aim of that extension was to bridge the gap between dialectic and rhetoric by the integration of a rhetorical dimension into the dialectical framework. Three main aspects of strategic maneuvering are explicitly addressed: argumentative choices from topical potential, adaptation to audience demand, and the employment of presentational devices. The article takes a closer look at the martial metaphor of strategic maneuvering. Examining the meaning and history of both « strategy » and « maneuvering » as well as the differences between strategy and tactics, it describes the positioning of each of the three aspects within the rhetorical system and the hierarchy between strategy and tactics. It thus shows how the choice of this metaphor frames the entire theory, and how the interpretation of fallacies as « derailments » of strategic maneuverings modifies the original pragma-dialectical concept. It turns out that the incorporation of strategic maneuvering into the pragma-dialectical system may itself be a strategy meant to immunize the theory against criticism.
Assumable plausibilities: Rhetoric as a doxastic technique of probability
2025-01-01

From the perspective of rhetorical theory, knowledge is primarily the result of a communicative dynamic of asserting and contesting claims to validity according to a range of normative criteria. This dynamic unfolds within a heterogeneous plurality of language games. Jean-François Lyotard characterizes this condition as a form of “general agonistics,” drawing on both philosophical and sophistic traditions. Against the backdrop of this general agonistics and Hans Blumenberg’s interpretation of Edmund Husserl’s theory of the lifeworld (Lebenswelt), the concept of doxa is examined and reconstructed as a fundamental rhetorical category for understanding the formation and justification of knowledge. Doxai emerge as plausibility potentials that can be assumed in concrete conflicts and serve as the basis for determining criteria of validity capable of communicatively addressing the conflict at hand. This becomes especially crucial in situations where validity conflicts cannot be resolved by reference to the normative frameworks of a single language game.
Evoked slow oscillations and dynamic network reorganization after stroke
2025-01-01

Abstract A focal ischemic lesion is thought to alter neuronal activity beyond the area of structural damage, thereby interfering with the whole network architecture. Here, we used a combination of transcranial magnetic stimulation and electroencephalography in conjunction with dynamic connectivity analyses and graph theory to study alterations and reorganization of cortical connectivity in a cohort of 41 patients longitudinally after stroke. We found a link between an increase in low-frequency coupling in the delta band and alterations in neural information processing in the first weeks after stroke and their relevance for motor outcome &amp;gt;3 months later. We demonstrated that stroke enhances slow activity and delta coupling between frontocentral and parietal regions. In addition, we observed a loss of the physiological network architecture with a decrease in small-worldness and modularity in the delta frequency, implying that a focal ischemic lesion interferes with both cortical information integration and functional segregation within the first weeks after stroke. While we found a link between bifrontal coupling in the alpha spectrum and the degree of the motor deficit in the early post-acute phase, the amount of small-worldness disruption early after stroke indicated the motor outcome in the follow-up session. In contrast, recovery of motor function and cortical reorganization after &amp;gt;3 months post-stroke were paralleled by the normalization of increased low-frequency coupling and a reinstatement of the complex network structure featuring a modular and small-world topology.
Interfacial halogen bonding with charge-transport layers for operational stability of hybrid perovskite solar cells
2025-01-01

We rely on halogen bonding at the interface with charge-transport layers using 1,4-diiodotetrafluorobenzene with TiO 2 and iodo-Zn–phthalocyanine with spiro-OMeTAD interfaces, showcasing the potential of halogen bonding in perovskite photovoltaics.
Multi-scale parameterization of neural rhythmicity with lagged Hilbert autocoherence
2025-01-01

Abstract Analysis of neural activity in different frequency bands is ubiquitous in systems and cognitive neuroscience. Recent analytical breakthroughs and theoretical developments rely on phase maintenance of oscillatory signals without considering whether or not this assumption is met. Lagged (auto)coherence, the coherence between a signal and itself at increasing temporal delays, has been proposed as a way to quantify the rhythmicity, or periodicity, of a signal. However, current Fourier-based lagged autocoherence algorithms suffer from poor spectral accuracy and resolution, aliasing effects that become more pronounced at higher frequencies, and conflation with amplitude covariation, especially in frequency ranges in which the signal-to-noise ratio is low. We introduce a continuous estimator, lagged Hilbert autocoherence (LHaC), which addresses these limitations by using multiplication in the frequency domain for precise bandpass filtering, computing instantaneous analytic signals via the Hilbert transform, and thresholding using the amplitude covariation of phase-shuffled surrogate data. While LHaC and lagged Fourier autocoherence (LFaC) estimate distinct theoretical quantities, we compare their empirical behavior in simulations with controlled rhythmic structure. These analyses show that LHaC provides more spectrally resolved estimates of rhythmicity and is more sensitive to the duration of transient, short-lived oscillatory events. We further demonstrate the utility of LHaC for identifying frequency-specific differences in rhythmicity between conditions and tracking learning-related changes in neural oscillations. Lagged Hilbert autocoherence thus offers a refined and practically useful approach to characterizing neurophysiological rhythmicity.
Traumatic Upper Extremity Nerve Lesions in Children: High-Resolution Nerve Ultrasound Can Improve Surgical Outcome
2025-01-01

Abstract Peripheral nerve injuries may accompany traumatic extremity injuries and are associated with significant morbidity. Diagnostic options are limited in young children since compliance might be restricted. High-resolution ultrasound (HRUS) is a promising technique to close the diagnostic gap, but clear recommendations are lacking. This study evaluates clinical outcomes after conservative versus surgical management, considering HRUS findings in pediatric patients with upper extremity peripheral nerve injuries. We retrospectively analyzed our pediatric neurosurgery database from August 2008 to December 2022 including patients &lt; 18 years with traumatic upper extremity nerve injury and excluding obstetrical brachial plexus injury. Systematic HRUS examinations were implemented from 2016 onwards. Clinical, intraoperative, sonographic and electrophysiological findings were assessed. A total of 73 nerve injuries in 67 patients (median age = 7.0 years) were analyzed. The most frequently affected nerves were the ulnar (49.3%), radial (21.9%), and median nerve (19.2%). At initial presentation, 47.9% underwent electrophysiology and 67.1% received HRUS. Surgery was performed in 49.3% at a median of 4 months posttrauma, whereas 50.7% were managed conservatively. Patients undergoing surgery initially had more severe sensory and motor deficits (χ2 = 3.98, p = 0.046), but final outcomes showed no significant difference in nerve function (median follow-up = 6.0 months). Binary logistic regression identified age (odds ratio [OR] = 1.3, p = 0.028), HRUS (OR = 10.6, p = 0.035), and injured nerve (OR = 3.1, p = 0.022) as independent outcome predictors. Good functional recovery in pediatric patients with peripheral nerve injury was demonstrated. HRUS-guided treatment and age &lt; 9 years were independent predictors of favorable outcome. These findings support HRUS as a valuable, noninvasive tool for guiding pediatric nerve injury management.
Initial Expectations and Confidence Affect the Formation of Novel Self-Beliefs and Their Revision
2025-01-01

Abstract Human self-beliefs hinge on social feedback, but their formation and revision are not solely based on new information. Biases during learning, such as confirming initial expectations, can lead to inaccurate beliefs. This study uses computational modeling to explore how initial expectations about one’s own and others’ abilities and confidence in these beliefs affect processes of belief formation and belief revision in novel behavioral domains. In the first session, participants formed performance beliefs through trial-by-trial feedback. In the second session, feedback contingencies were reversed to promote a revision of beliefs. Results showed that people form and revise beliefs in a confirmatory manner, with lower initial expectations being linked to more negatively biased belief formation and revision, while growing confidence strengthened these beliefs over time. Once formed, these beliefs proved resistant to change even when faced with contradictory feedback. The findings suggest that newly formed beliefs become entrenched and resistant to new, contradictory information in a short period of time. Understanding how self-beliefs are formed, the role that confidence plays in this process, and why established beliefs are difficult to revise can inform the development of interventions aimed at promoting more adaptive learning in educational, clinical, and social contexts.
Structural modifications of M 5 O 4 I 11 ( M = Nb, Ta) cluster networks from heterogeneous solid-state reactions
2025-01-01

A series of cluster compounds sharing a [ M 5 O 4 ] cluster core ( M = Nb, Ta) is obtained from heterogeneous solid-state reactions. They show semiconducting behaviour with ionic to covalent bonding, according to electronic structure calculations.
Proteomic and Metabolomic Signatures in Prediabetes Progressing to Diabetes or Reversing to Normoglycemia Within 1 Year
2025-01-02

OBJECTIVE Progression of prediabetes to type 2 diabetes has been associated with β-cell dysfunction, whereas its remission to normoglycemia has been related to improvement of insulin sensitivity. To understand the mechanisms and identify potential biomarkers related to prediabetes trajectories, we compared the proteomics and metabolomics profile of people with prediabetes progressing to diabetes or reversing to normoglycemia within 1 year. RESEARCH DESIGN AND METHODS The fasting plasma concentrations of 1,389 proteins and the fasting, 30-min, and 120-min post–oral glucose tolerance test (OGTT) plasma concentrations of 152 metabolites were measured in up to 134 individuals with new-onset diabetes, prediabetes, or normal glucose tolerance. For 108 participants, the analysis was repeated with samples from 1 year before, when all had prediabetes. RESULTS The plasma concentrations of 14 proteins were higher in diabetes compared with normoglycemia in a population with prediabetes 1 year before, and they correlated with indices of insulin sensitivity. Higher levels of dicarbonyl/L-xylulose reductase and glutathione S-transferase A3 in the prediabetic state were associated with an increased risk of diabetes 1 year later. Pathway analysis pointed toward differences in immune response between diabetes and normoglycemia that were already recognizable in the prediabetic state 1 year prior at baseline. The area under the curve during OGTT of the concentrations of IDL particles, IDL apolipoprotein B, and IDL cholesterol was higher in new-onset diabetes compared with normoglycemia. The concentration of glutamate increased in prediabetes progressing to diabetes. CONCLUSIONS We identify new candidates associated with the progression of prediabetes to diabetes or its remission to normoglycemia. Pathways regulating the immune response are related to prediabetes trajectories.
Retrieval Augmented Therapy Suggestion for Molecular Tumor Boards: Algorithmic Development and Validation Study
2025-01-02

Background Molecular tumor boards (MTBs) require intensive manual investigation to generate optimal treatment recommendations for patients. Large language models (LLMs) can catalyze MTB recommendations, decrease human error, improve accessibility to care, and enhance the efficiency of precision oncology. Objective In this study, we aimed to investigate the efficacy of LLM-generated treatments for MTB patients. We specifically investigate the LLMs’ ability to generate evidence-based treatment recommendations using PubMed references. Methods We built a retrieval augmented generation pipeline using PubMed data. We prompted the resulting LLM to generate treatment recommendations with PubMed references using a test set of patients from an MTB conference at a large comprehensive cancer center at a tertiary care institution. Members of the MTB manually assessed the relevancy and correctness of the generated responses. Results A total of 75% of the referenced articles were properly cited from PubMed, while 17% of the referenced articles were hallucinations, and the remaining were not properly cited from PubMed. Clinician-generated LLM queries achieved higher accuracy through clinician evaluation than automated queries, with clinicians labeling 25% of LLM responses as equal to their recommendations and 37.5% as alternative plausible treatments. Conclusions This study demonstrates how retrieval augmented generation–enhanced LLMs can be a powerful tool in accelerating MTB conferences, as LLMs are sometimes capable of achieving clinician-equal treatment recommendations. However, further investigation is required to achieve stable results with zero hallucinations. LLMs signify a scalable solution to the time-intensive process of MTB investigations. However, LLM performance demonstrates that they must be used with heavy clinician supervision, and cannot yet fully automate the MTB pipeline.
Metabolic mutations reduce antibiotic susceptibility of E. coli by pathway-specific bottlenecks
2025-01-02

Abstract Metabolic variation across pathogenic bacterial strains can impact their susceptibility to antibiotics and promote the evolution of antimicrobial resistance (AMR). However, little is known about how metabolic mutations influence metabolism and which pathways contribute to antibiotic susceptibility. Here, we measured the antibiotic susceptibility of 15,120 Escherichia coli mutants, each with a single amino acid change in one of 346 essential proteins. Across all mutants, we observed modest increases of the minimal inhibitory concentration (twofold to tenfold) without any cases of major resistance. Most mutants that showed reduced susceptibility to either of the two tested antibiotics carried mutations in metabolic genes. The effect of metabolic mutations on antibiotic susceptibility was antibiotic- and pathway-specific: mutations that reduced susceptibility against the β-lactam antibiotic carbenicillin converged on purine nucleotide biosynthesis, those against the aminoglycoside gentamicin converged on the respiratory chain. In addition, metabolic mutations conferred tolerance to carbenicillin by reducing growth rates. These results, along with evidence that metabolic bottlenecks are common among clinical E. coli isolates, highlight the contribution of metabolic mutations for AMR.
Real‐world management of patients with complete response under immune‐checkpoint inhibition for advanced melanoma
2025-01-02

Summary Background Up to now, the optimal duration of immune checkpoint inhibitors (ICI) has not been evaluated in prospective studies. However, current clinical practice requires decisions to be made regarding the duration of ICI in complete responders. Material and Methods A survey was sent to 80 DeCOG skin cancer centers to assess how decisions are made on treatment duration of ICI in melanoma after having reached complete response, and staging intervals after ICI discontinuation. All responses received by March 10, 2024 (51 centers) were included. Results The duration of ICI after having achieved complete remission varies between centers from three to 36 months. In total, 66% of the DeCOG centers continue treatment for up to 6 months, after having achieved complete remission (CR) with ICI. In the first year after discontinuation of ICI, most centers perform staging intervals (CT/MRI) every 3 months. More than 60% of centers continue staging at least once per year even in the 4th and 5th year after discontinuation. Conclusions There are significant differences between the centers regarding staging intervals and duration of ICI upon CR. Prospective studies are necessary to determine the optimal time point of ICI discontinuation and follow‐up.
Goodbye human annotators? Content analysis of social policy debates using ChatGPT
2025-01-03

Abstract Content analysis is a valuable tool for analysing policy discourse, but annotation by humans is costly and time consuming. ChatGPT is a potentially valuable tool to partially automate content analysis for policy debates, largely replacing human annotators. We evaluate ChatGPT’s ability to classify documents using pre-defined argument descriptions, comparing its performance with human annotators for two policy debates: the Universal Basic Income debate on Dutch Twitter (2014–2016) and the pension reforms debate in German newspapers (1993–2001). We use the API (GPT-4 Turbo) and user interface version (GPT-4) and evaluate multiple performance metrics (accuracy, precision and recall). ChatGPT is highly reliable and accurate in classifying pre-defined arguments across datasets. However, precision and recall are much lower, and vary strongly between arguments. These results hold for both datasets, despite differences in language and media type. Moreover, the cut-off method proposed in this paper may aid researchers in navigating the trade-off between detection and noise. Overall, we do not (yet) recommend a blind application of ChatGPT to classify arguments in policy debates. Those interested in adopting this tool should manually validate bot classifications before using them in further analyses. At least for now, human annotators are here to stay.
Genome-wide CRISPR-Cas9 screens identify BCL family members as modulators of response to regorafenib in experimental glioma
2025-01-03

Abstract Background Registered systemic treatment options for glioblastoma patients are limited. The phase II REGOMA trial suggested an improvement of median overall survival in progressive glioblastoma by the multi-tyrosine kinase inhibitor regorafenib. This has not been confirmed by GBM AGILE. So far, regorafenib has been administered as monotherapy or as an addition to standard of care in newly diagnosed glioblastoma. Rational combination therapies involving regorafenib might be a reasonable strategy. Here, we aimed at identifying functionally instructed combination therapies involving regorafenib. Methods We applied a genome-wide CRISPR-Cas9-based functional genomics target discovery approach using activation and knockout screens followed by genetic, pharmacological, functional validations. Regorafenib-induced molecular alterations were assessed by RNA sequencing and DigiWest. We investigated selected functionally instructed combination therapies in three orthotopic glioma mouse models in vivo (syngeneic SMA560/VM/Dk model and two xenograft models) and performed immunohistochemistry of post-treatment brains. Results We identified potential modifiers of regorafenib response, including BCL2, BCL2L1, ITGB3, FOXC1, SERAC1, ARAF, and PLCE1. The combination of regorafenib with Bcl-2/Bcl-xL inhibition was superior to both monotherapies alone in vitro, ex vivo, and in vivo. We identified regorafenib-induced regulations of the Bcl-2 downstream target chemokine receptor 1 (CCR1) as one potential underlying molecular mediator. Furthermore, regorafenib led to changes in the myeloid compartment of the glioma-associated microenvironment. Conclusions This preclinical study uses a functional genomics-based target discovery approach with subsequent validations involving regorafenib. It serves as a biological rationale for clinical translation. Particularly, an investigation of the combination of regorafenib plus navitoclax within a clinical trial is warranted.