Metabolic and cardiac adaptation to chronic pharmacologic blockade of facilitative glucose transport in murine dilated cardiomyopathy and myocardial ischemia

Abstract GLUT transgenic and knockout mice have provided valuable insight into the role of facilitative glucose transporters (GLUTs) in cardiovascular and metabolic disease, but compensatory physiological changes can hinder interpretation of these models. To determine whether adaptations occur in response to GLUT inhibition in the failing adult heart, we chronically treated TG9 mice, a transgenic model of dilated cardiomyopathy and heart failure, with the GLUT inhibitor ritonavir. Glucose tolerance was significantly improved with chronic treatment and correlated with decreased adipose tissue retinol binding protein 4 (RBP4) and resistin. A modest improvement in lifespan was associated with decreased cardiomyocyte brain natriuretic peptide (BNP) expression, a marker of heart failure severity. GLUT1 and −12 protein expression was significantly increased in left ventricular (LV) myocardium in ritonavir-treated animals. Supporting a switch from fatty acid to glucose utilization in these tissues, fatty acid transporter CD36 and fatty acid transcriptional regulator peroxisome proliferator-activated receptor α (PPARα) mRNA were also decreased in LV and soleus muscle. Chronic ritonavir also increased cardiac output and dV/dt-d in C57Bl/6 mice following ischemia-reperfusion injury. Taken together, these data demonstrate compensatory metabolic adaptation in response to chronic GLUT blockade as a means to evade deleterious changes in the failing heart

Language with Vision: a Study on Grounded Word and Sentence Embeddings

Grounding language in vision is an active field of research seeking to construct cognitively plausible word and sentence representations by incorporating perceptual knowledge from vision into text-based representations. Despite many attempts at language grounding, achieving an optimal equilibrium between textual representations of the language and our embodied experiences remains an open field. Some common concerns are the following. Is visual grounding advantageous for abstract words, or is its effectiveness restricted to concrete words? What is the optimal way of bridging the gap between text and vision? To what extent is perceptual knowledge from images advantageous for acquiring high-quality embeddings? Leveraging the current advances in machine learning and natural language processing, the present study addresses these questions by proposing a simple yet very effective computational grounding model for pre-trained word embeddings. Our model effectively balances the interplay between language and vision by aligning textual embeddings with visual information while simultaneously preserving the distributional statistics that characterize word usage in text corpora. By applying a learned alignment, we are able to indirectly ground unseen words including abstract words. A series of evaluations on a range of behavioural datasets shows that visual grounding is beneficial not only for concrete words but also for abstract words, lending support to the indirect theory of abstract concepts. Moreover, our approach offers advantages for contextualized embeddings, such as those generated by BERT, but only when trained on corpora of modest, cognitively plausible sizes. Code and grounded embeddings for English are available at https://github.com/Hazel1994/Visually_Grounded_Word_Embeddings_2

How direct is the link between words and images?

Current word embedding models despite their success, still suffer from their lack of grounding in the real world. In this line of research, Gunther et al. 2022 proposed a behavioral experiment to investigate the relationship between words and images. In their setup, participants were presented with a target noun and a pair of images, one chosen by their model and another chosen randomly. Participants were asked to select the image that best matched the target noun. In most cases, participants preferred the image selected by the model. Gunther et al., therefore, concluded the possibility of a direct link between words and embodied experience. We took their experiment as a point of departure and addressed the following questions. 1. Apart from utilizing visually embodied simulation of given images, what other strategies might subjects have used to solve this task? To what extent does this setup rely on visual information from images? Can it be solved using purely textual representations? 2. Do current visually grounded embeddings explain subjects' selection behavior better than textual embeddings? 3. Does visual grounding improve the semantic representations of both concrete and abstract words? To address these questions, we designed novel experiments by using pre-trained textual and visually grounded word embeddings. Our experiments reveal that subjects' selection behavior is explained to a large extent based on purely text-based embeddings and word-based similarities, suggesting a minor involvement of active embodied experiences. Visually grounded embeddings offered modest advantages over textual embeddings only in certain cases. These findings indicate that the experiment by Gunther et al. may not be well suited for tapping into the perceptual experience of participants, and therefore the extent to which it measures visually grounded knowledge is unclear.Comment: Accepted in the Mental Lexicon Journal: https://benjamins.com/catalog/m

A novel FRET-based screen in high-throughput format to identify inhibitors of malarial and human glucose transporters

The glucose transporter PfHT is essential to the survival of the malaria parasite Plasmodium falciparum and has been shown to be a druggable target with high potential for pharmacological intervention. Identification of compounds against novel drug targets is crucial to combating resistance against current therapeutics. Here, we describe the development of a cell-based assay system readily adaptable to high-throughput screening that directly measures compound effects on PfHT-mediated glucose transport. Intracellular glucose concentrations are detected using a genetically encoded fluorescence resonance energy transfer (FRET)-based glucose sensor. This allows assessment of the ability of small molecules to inhibit glucose uptake with high accuracy (Z′ factor of >0.8), thereby eliminating the need for radiolabeled substrates. Furthermore, we have adapted this assay to counterscreen PfHT hits against the human orthologues GLUT1, -2, -3, and -4. We report the identification of several hits after screening the Medicines for Malaria Venture (MMV) Malaria Box, a library of 400 compounds known to inhibit erythrocytic development of P. falciparum. Hit compounds were characterized by determining the half-maximal inhibitory concentration (IC(50)) for the uptake of radiolabeled glucose into isolated P. falciparum parasites. One of our hits, compound MMV009085, shows high potency and orthologue selectivity, thereby successfully validating our assay for antimalarial screening

Exploring the influence of semantics on the German plural system: a wug study

The role of semantics in inflectional morphology has long been debated (Huang & Pinker, 2010; Pinker & Prince, 1988; Ramscar, 2002) with most of the focus on the English past tense. This paper explores whether an effect of semantics can be found for German noun plural generalisation, a system as yet only poorly understood. German speakers were asked to first freely produce and then rate plural forms of 24 new wug words, presented in a semantically manipulated context. We expected that the German plural class ending in -n should be used more frequently with nouns presented as persons than as objects (Gaeta, 2008). While this hypothesis was not confirmed, the post-hoc discovery of other semantic influences prevents us from completely rejecting the original hypothesis. In light of these results we discuss possible sources of the observed pattern of plural classes and stress the importance of replicating wug studies with novel sets of wug words. We conclude that generalisation of the German plural system cannot easily be explained by phonological nor semantic influences

How trial-to-trial learning shapes mappings in the mental lexicon: Modelling Lexical Decision with Linear Discriminative Learning

Priming and antipriming can be modelled with error-driven learning (Marsolek, 2008), by assuming that the learning of the prime influences processing of the target stimulus. This implies that participants are continuously learning in priming studies, and predicts that they are also learning in each trial of other psycholinguistic experiments. This study investigates whether trial-to-trial learning can be detected in lexical decision experiments. We used the Discriminative Lexicon Model (DLM; Baayen et al., 2019), a model of the mental lexicon with meaning representations from distributional semantics, which models incremental learning with the Widrow-Hoff rule. We used data from the British Lexicon Project (BLP; Keuleers et al., 2012) and simulated the lexical decision experiment with the DLM on a trial-by-trial basis for each subject individually. Then, reaction times for words and nonwords were predicted with Generalised Additive Models, using measures derived from the DLM simulations as predictors. Models were developed with the data of two subjects and tested on all other subjects. We extracted measures from two simulations for each subject (one with learning updates between trials and one without), and used them as input to two GAMs. Learning-based models showed better model fit than the non-learning ones for the majority of subjects. Our measures also provided insights into lexical processing and enabled us to explore individual differences with Linear Mixed Models. This demonstrates the potential of the DLM to model behavioural data and leads to the conclusion that trial-to-trial learning can indeed be detected in psycholinguistic experiments.Comment: 38 pages, 13 figure

Modeling Morphology With Linear Discriminative Learning: Considerations and Design Choices

This study addresses a series of methodological questions that arise when modeling inflectional morphology with Linear Discriminative Learning. Taking the semi-productive German noun system as example, we illustrate how decisions made about the representation of form and meaning influence model performance. We clarify that for modeling frequency effects in learning, it is essential to make use of incremental learning rather than the endstate of learning. We also discuss how the model can be set up to approximate the learning of inflected words in context. In addition, we illustrate how in this approach the wug task can be modeled in considerable detail. In general, the model provides an excellent memory for known words, but appropriately shows more limited performance for unseen data, in line with the semi-productivity of German noun inflection and generalization performance of native German speakers.Comment: 38 pages, 5 figures, 10 tables; acknowledgements adde