Word naming slows picture naming but does not affect cumulative semantic interference

Two experiments are reported which investigate the effect of processing words prior to naming target pictures. In Experiment 1, participants named (read aloud) sequences of five printed prime words and five target pictures from the same semantic category, and also sequences of five prime words from a different unrelated semantic category to the five related target pictures. Picture and words were interleaved, with two unrelated filler stimuli in between prime and target stimuli (i.e. a lag of 3 between primes and targets). Results showed that across the five target picture naming trials (i.e. across ordinal position of picture), picture naming times increased linearly, replicating the cumulative semantic interference (CSI) effect (e.g., Howard, Nickels, Coltheart, & Cole-Virtue, 2006). Related prime words slowed picture naming, replicating the effects found in paired word prime and picture target studies (e.g., Tree & Hirsh, 2003). However, the naming of the five related prime words did not modify the picture naming CSI effect, with this null result converging with findings from a different word and picture design (e.g., Navarrete, Mahon, & Caramazza, 2010). In Experiment 2, participants categorised the prime word stimuli as manmade versus natural, so that words were more fully processed at a conceptual level. The interaction between word prime relatedness and ordinal position of the named target picture was significant. These results are consistent with adjustments at the conceptual level (Belke, 2013; Roelofs, 2018) which last over several trials at least. By contrast, we conclude that the distinct word-to-picture naming interference effect from Experiment 1 must originate outside of the conceptual level and outside of the mappings between semantics and lexical representations. We discuss the results with reference to recent theoretical accounts of the CSI picture naming effect and word naming models

Clinical Symptoms and Histological Changes in Poecilia reticulata following Gamma-Ray Irradiation

Lethality, food intake, clinical symptoms and terminal histological changes were followed after gamma-irradiation with doses of 10, 20, 30, 35 and 40 Gy in guppy, Poecilia reticulata. Clinical symptoms, food intake and longevity were also monitored in the progeny of fish irradiated with a dose of 10 Gy. In the first days after irradiation timidity and lethargy were observed. After doses of 30, 35 and 40 Gy, these symptoms were accompanied with anorexia. The most prominent clinical symptoms observed were emaciation, hampered breathing, exophthalmia and haemorrhages. Histological findings corresponded with these symptoms. In the fish irradiated with 10 or 20 Gy the progeny survived, after a dose of 30 Gy the progeny died within 24 hours after birth and after doses of 35 and 40 Gy dead progeny was born. The survival data provide an estimate of LD50/30 equal to 29 Gy. Higher relative mortality and more severe clinical symptoms were observed in females. The progeny of irradiated parent fish grew and survived for 3 months maximum

In Vivo Assessment of Zearalenone Toxicity

The microscopic filamentous fungi of the genus Fusarium are capable of producing secondary metabolites—mycotoxins. Fusarium fungi synthesize trichothecenes, zearalenone (ZEA) and fumonisins under appropriate environmental conditions. In this biological experiment, we studied the effects of zearalenone on a model organism called Artemia franciscana. During the three-day in vivo tests, we used five different concentrations of zearalenone (0.08 ppm, 0.4 ppm, 2 ppm, 10 ppm and 50 ppm). The results of this study showed that as the zearalenone concentration and the duration of the mycotoxin exposure increased, the lethality of artemia also increased. Our study showed that the toxicity of zearalenone to Artemia franciscana was relatively low

Horses to zebras: Ontology-guided data augmentation and synthesis for ICD-9 coding

Medical document coding is the process of assigning labels from a structured label space (ontology &ndash; e.g., ICD-9) to medical documents. This process is laborious, costly, and errorprone. In recent years, efforts have been made to automate this process with neural models. The label spaces are large (in the order of thousands of labels) and follow a big-head long-tail label distribution, giving rise to few-shot and zero-shot scenarios. Previous efforts tried to address these scenarios within the model, leading to improvements on rare labels, but worse results on frequent ones. We propose data augmentation and synthesis techniques in order to address these scenarios. We further introduce an analysis technique for this setting inspired by confusion matrices. This analysis technique points to the positive impact of data augmentation and synthesis, but also highlights more general issues of confusion within families of codes, and underprediction.</p

Protective Role of Agrimonia eupatoria L. in Heavy Metal Induced Nephrotoxicity

The aim of this study was to evaluate the potential protective role of Agrimonia eupatoria L. in heavy metal induced nephrotoxicity. Rabbit kidney epithelial cells (RK13) were used as the model cell line. They were exposed to three different heavy metal compounds: cadmium chloride dihydrate CdCl2.2H2O (15 and 20 mg.l−1), potassium dichromate K2Cr2O7 (1, 10 mg.l−1), and zinc sulfate heptahydrate ZnSO4.7H2O (50, 150 mg.l−1) simultaneously with agrimony (ethanolic extract, 100 mg.l−1). The cell response was recorded using the xCELLigence system or real-time cell analysis (RTCA) as a cell index (CI) and expressed as cell adherence (%) compared to control cells without treatment. The potential nephroprotective effects were recorded in cells treated with chromium (1 a 10 mg.l−1) and agrimony, where the cell adherence increased from 95.11 ± 11.25 % and 7.24 ± 0.33 % to 103.26 ± 1.23 % and 68.54 ± 4.89 % (P < 0.05) respectfully and also with a combination of agrimony and zinc (150 mg.l−1), where the adherence increased from 57.45 ± 1.98 % to 95.4 ± 6.95 %. During the cell exposure to cadmium in combination with agrimony, the protective effect was not recorded; the adherence of cells was even decreased (P < 0.05)

Automated clinical coding: what, why, and where we are?

Clinical coding is the task of transforming medical information in a patient's health records into structured codes so that they can be used for statistical analysis. This is a cognitive and time-consuming task that follows a standard process in order to achieve a high level of consistency. Clinical coding could potentially be supported by an automated system to improve the efficiency and accuracy of the process. We introduce the idea of automated clinical coding and summarise its challenges from the perspective of Artificial Intelligence (AI) and Natural Language Processing (NLP), based on the literature, our project experience over the past two and half years (late 2019-early 2022), and discussions with clinical coding experts in Scotland and the UK. Our research reveals the gaps between the current deep learning-based approach applied to clinical coding and the need for explainability and consistency in real-world practice. Knowledge-based methods that represent and reason the standard, explainable process of a task may need to be incorporated into deep learning-based methods for clinical coding. Automated clinical coding is a promising task for AI, despite the technical and organisational challenges. Coders are needed to be involved in the development process. There is much to achieve to develop and deploy an AI-based automated system to support coding in the next five years and beyond