Aqueous ozonation of furans:Kinetics and transformation mechanisms leading to the formation of α,β-unsaturated dicarbonyl compounds

Despite the widespread occurrence of furan moieties in synthetic and natural compounds, their fate in aqueous ozonation has not been investigated in detail. Reaction rate constants of seven commonly used furans with ozone were measured and ranged from kO3 = 8.5 × 104 to 3.2 × 106 M−1 s−1, depending on the type and position of furan ring substituents. Transformation product analysis of the reaction of furans with ozone focusing on the formation of toxic organic electrophiles using a novel amino acid reactivity assay revealed the formation of α,β-unsaturated dicarbonyl compounds, 2-butene-1,4-dial (BDA) and its substituted analogues (BDA-Rs). Their formation can be attributed to ozone attack at the reactive α-C position leading to furan ring opening. The molar yields of α,β-unsaturated dicarbonyl compounds varied with the applied ozone concentration reaching maximum values of 7% for 2-furoic acid. The identified α,β-unsaturated dicarbonyls are well-known toxicophores that are also formed by enzymatic oxidation of furans in the human body. In addition to providing data on kinetics, transformation product analysis and proposed reaction mechanisms for the ozonation of furans, this study raises concern about the presence of α,β-unsaturated dicarbonyl compounds in water treatment and the resulting effects on human and environmental health.</p

Oculomotoric Biometric Identification under the Influence of Alcohol and Fatigue

Patterns of micro- and macro-movements of the eyes are highly individual and can serve as a biometric characteristic. It is also known that both alcohol inebriation and fatigue can reduce saccadic velocity and accuracy. This prompts the question of whether changes of gaze patterns caused by alcohol consumption and fatigue impact the accuracy of oculomotoric biometric identification. We collect an eye tracking data set from 66 participants in sober, fatigued and alcohol-intoxicated states. We find that after enrollment in a rested and sober state, identity verification based on a deep neural embedding of gaze sequences is significantly less accurate when probe sequences are taken in either an inebriated or a fatigued state. Moreover, we find that fatigue and intoxication appear to randomize gaze patterns: when the model is fine-tuned for invariance with respect to inebriation and fatigue, and even when it is trained exclusively on inebriated training person, the model still performs significantly better for sober than for sleep-deprived or intoxicated subjects

Macrophage-Derived Biomarkers of Idiopathic Pulmonary Fibrosis

Idiopathic pulmonary fibrosis (IPF) is a severe, rapidly progressive diffuse lung disease. Several pathogenetic mechanisms have been hypothesized on the basis of the fibrotic lung damage occurring in this disease, and a potential profibrotic role of activated alveolar macrophages and their mediators in the pathogenesis of IPF was recently documented. This paper focuses on recent literature on potential biomarkers of IPF derived from activated alveolar macrophages. Biomarker discovery and clinical application are a recent topic of interest in the field of interstitial lung diseases (ILDs). Cytokines, CC-chemokines, and other macrophage-produced mediators are the most promising prognostic biomarkers. Many molecules have been proposed in the literature as potential biomarker of IPF; however, a rigorous validation is needed to confirm their clinical utility

Pre-Trained Language Models Augmented with Synthetic Scanpaths for Natural Language Understanding

Human gaze data offer cognitive information that reflects natural language comprehension. Indeed, augmenting language models with human scanpaths has proven beneficial for a range of NLP tasks, including language understanding. However, the applicability of this approach is hampered because the abundance of text corpora is contrasted by a scarcity of gaze data. Although models for the generation of human-like scanpaths during reading have been developed, the potential of synthetic gaze data across NLP tasks remains largely unexplored. We develop a model that integrates synthetic scanpath generation with a scanpath-augmented language model, eliminating the need for human gaze data. Since the model's error gradient can be propagated throughout all parts of the model, the scanpath generator can be fine-tuned to downstream tasks. We find that the proposed model not only outperforms the underlying language model, but achieves a performance that is comparable to a language model augmented with real human gaze data. Our code is publicly available.Comment: Pre-print for EMNLP 202

Fairness in Oculomotoric Biometric Identification

Gaze patterns are known to be highly individual, and therefore eye movements can serve as a biometric characteristic. We explore aspects of the fairness of biometric identification based on gaze patterns. We find that while oculomotoric identification does not favor any particular gender and does not significantly favor by age range, it is unfair with respect to ethnicity. Moreover, fairness concerning ethnicity cannot be achieved by balancing the training data for the best-performing model

Pre-Trained Language Models Augmented with Synthetic Scanpaths for Natural Language Understanding

Human gaze data offer cognitive information that reflects natural language comprehension. Indeed, augmenting language models with human scanpaths has proven beneficial for a range of NLP tasks, including language understanding. However, the applicability of this approach is hampered because the abundance of text corpora is contrasted by a scarcity of gaze data. Although models for the generation of human-like scanpaths during reading have been developed, the potential of synthetic gaze data across NLP tasks remains largely unexplored. We develop a model that integrates synthetic scanpath generation with a scanpath-augmented language model, eliminating the need for human gaze data. Since the model’s error gradient can be propagated throughout all parts of the model, the scanpath generator can be fine-tuned to downstream tasks. We find that the proposed model not only outperforms the underlying language model, but achieves a performance that is comparable to a language model augmented with real human gaze data. Our code is publicly available

Selection of XAI Methods Matters: Evaluation of Feature Attribution Methods for Oculomotoric Biometric Identification

Substantial advances in oculomotoric biometric identification have been made due to deep neural networks processing non-aggregated time series data that replace methods processing theoretically motivated engineered features. However, interpretability of deep neural networks is not trivial and needs to be thoroughly investigated for future eye tracking applications. Especially in medical or legal applications explanations can be required to be provided alongside predictions. In this work, we apply several attribution methods to a state of the art model for eye movement-based biometric identification. To asses the quality of the generated attributions, this work is focused on the quantitative evaluation of a range of established metrics. We find that Layer-wise Relevance Propagation generates the least complex attributions, while DeepLIFT attributions are the most faithful. Due to the absence of a correlation between attributions of these two methods we advocate to consider both methods for their potentially complementary attributions

Eyettention: An Attention-based Dual-Sequence Model for Predicting Human Scanpaths during Reading

Eye movements during reading offer insights into both the reader's cognitive processes and the characteristics of the text that is being read. Hence, the analysis of scanpaths in reading have attracted increasing attention across fields, ranging from cognitive science over linguistics to computer science. In particular, eye-tracking-while-reading data has been argued to bear the potential to make machine-learning-based language models exhibit a more human-like linguistic behavior. However, one of the main challenges in modeling human scanpaths in reading is their dual-sequence nature: the words are ordered following the grammatical rules of the language, whereas the fixations are chronologically ordered. As humans do not strictly read from left-to-right, but rather skip or refixate words and regress to previous words, the alignment of the linguistic and the temporal sequence is non-trivial. In this paper, we develop Eyettention, the first dual-sequence model that simultaneously processes the sequence of words and the chronological sequence of fixations. The alignment of the two sequences is achieved by a cross-sequence attention mechanism. We show that Eyettention outperforms state-of-the-art models in predicting scanpaths. We provide an extensive within- and across-data set evaluation on different languages. An ablation study and qualitative analysis support an in-depth understanding of the model's behavior

SP-EyeGAN: Generating Synthetic Eye Movement Data with Generative Adversarial Networks

Neural networks that process the raw eye-tracking signal can outperform traditional methods that operate on scanpaths preprocessed into fixations and saccades. However, the scarcity of such data poses a major challenge. We, therefore, present SP-EyeGAN, a neural network that generates synthetic raw eye-tracking data. SP-EyeGAN consists of Generative Adversarial Networks; it produces a sequence of gaze angles indistinguishable from human micro- and macro-movements. We demonstrate how the generated synthetic data can be used to pre-train a model using contrastive learning. This model is fine-tuned on labeled human data for the task of interest. We show that for the task of predicting reading comprehension from eye movements, this approach outperforms the previous state-of-the-art