An increasing number of studies investigate the visual mismatch negativity (vMMN) or use the vMMN as a tool to probe various aspects of human cognition. This paper reviews the theoretical underpinnings of vMMN in the light of methodological considerations and provides recommendations for measuring and interpreting the vMMN. The following key issues are discussed from the experimentalist's point of view in a predictive coding framework: (1) experimental protocols and procedures to control "refractoriness" effects; (2) methods to control attention; (3) vMMN and veridical perception.

• Keywords
• EEG, ERP, perceptual learning, prediction error, predictive coding, repetition suppression, stimulus specific adaptation, visual mismatch negativity,
• Publication type
• Journal Article MeSH
• Review MeSH

Incomprehension of and resistance to contemporaneous art have been constant features in the development of modern art. The predictive coding framework can be used to analyse this response by outlining the difference between the misunderstanding of (i) contemporary conceptual/minimalist art and (ii) early modern avant-garde art and by elucidating their underlying cognitive mechanisms. In both of these cases, incomprehension and its behavioural consequences are tied to the failure of the optimal prediction error (PE) minimization that is involved in the perception of such art works. In the case of contemporary conceptual/minimalist art the failure stems from the fact that the encounter results in non-salient visual sensations and generates no PE. In early modern avant-garde art, the occasional inability of viewers to recognize pictorial content using new pictorial conventions reflected the absence of suitable priors to explain away ambiguous sensory data. The capacity to recognize pictorial content in modernist painting, as a prerequisite for a satisfying encounter with such works and ultimately a wider acceptance of new artistic styles, required an updating of a number of expectations in order to optimize the fit between priors and sensations, from low-level perceptual priors to the development of higher-level, culturally determined expectations. This article is part of the theme issue 'Art, aesthetics and predictive processing: theoretical and empirical perspectives'.

• Keywords
• contemporary art, hyperprior, incomprehension, modern art, predictive coding, recognition,
• MeSH
• Sensation MeSH
• Brain MeSH
• Art * MeSH
• Publication type
• Journal Article MeSH

An important brain function is to predict upcoming events on the basis of extracted regularities of previous inputs. These predictive coding processes can disturb performance in concurrent perceptual decision-making and are known to depend on fronto-striatal circuits. However, it is unknown whether, and if so, to what extent striatal microstructural properties modulate these processes. We addressed this question in a human disease model of striosomal dysfunction, i.e. X-linked dystonia-parkinsonism (XDP), using high-density EEG recordings and source localization. The results show faster and more accurate perceptual decision-making performance during distraction in XDP patients compared to healthy controls. The electrophysiological data show that sensory memory and predictive coding processes reflected by the mismatch negativity related to lateral prefrontal brain regions were weakened in XDP patients and thus induced less cognitive conflict than in controls as reflected by the N2 event-related potential (ERP). Consequently, attentional shifting (P3a ERP) and reorientation processes (RON ERP) were less pronounced in the XDP group. Taken together, these results suggests that striosomal dysfunction is related to predictive coding deficits leading to a better performance in concomitant perceptual decision-making, probably because predictive coding does not interfere with perceptual decision-making processes. These effects may reflect striatal imbalances between the striosomes and the matrix compartment.

• Keywords
• Basal ganglia, EEG, Perceptual decision making, Predictive coding, Sensory memory, Striosomes, X-linked dystonia parkinsonism,
• MeSH
• Corpus Striatum physiopathology   MeSH
• Adult MeSH
• Dystonic Disorders physiopathology   psychology   MeSH
• Electroencephalography MeSH
• Evoked Potentials MeSH
• Genetic Diseases, X-Linked physiopathology   psychology   MeSH
• Middle Aged MeSH
• Humans MeSH
• Brain physiopathology   MeSH
• Reaction Time MeSH
• Decision Making physiology   MeSH
• Check Tag
• Adult MeSH
• Middle Aged MeSH
• Humans MeSH
• Male MeSH
• Publication type
• Journal Article MeSH

The International Classification of Diseases (ICD) hierarchical taxonomy is used for so-called clinical coding of medical reports, typically presented in unstructured text. In the Czech Republic, it is currently carried out manually by a so-called clinical coder. However, due to the human factor, this process is error-prone and expensive. The coder needs to be properly trained and spends significant effort on each report, leading to occasional mistakes. The main goal of this paper is to propose and implement a system that serves as an assistant to the coder and automatically predicts diagnosis codes. These predictions are then presented to the coder for approval or correction, aiming to enhance efficiency and accuracy. We consider two classification tasks: main (principal) diagnosis; and all diagnoses. Crucial requirements for the implementation include minimal memory consumption, generality, ease of portability, and sustainability. The main contribution lies in the proposal and evaluation of ICD classification models for the Czech language with relatively few training parameters, allowing swift utilisation on the prevalent computer systems within Czech hospitals and enabling easy retraining or fine-tuning with newly available data. First, we introduce a small transformer-based model for each task followed by the design of a transformer-based "Four-headed" model incorporating four distinct classification heads. This model achieves comparable, sometimes even better results, against four individual models. Moreover this novel model significantly economises memory usage and learning time. We also show that our models achieve comparable results against state-of-the-art English models on the Mimic IV dataset even though our models are significantly smaller.

• Keywords
• Coding, Diagnosis coding, ICD, Medical, Text classification,
• MeSH
• Electronic Health Records MeSH
• Clinical Coding * MeSH
• Humans MeSH
• International Classification of Diseases * MeSH
• Check Tag
• Humans MeSH
• Publication type
• Journal Article MeSH
• Research Support, Non-U.S. Gov't MeSH
• Geographicals
• Czech Republic MeSH

Among the main challenges of the predictive brain/mind concept is how to link prediction at the neural level to prediction at the cognitive-psychological level and finding conceptually robust and empirically verifiable ways to harness this theoretical framework toward explaining higher-order mental and cognitive phenomena, including the subjective experience of aesthetic and symbolic forms. Building on the tentative prediction error account of visual art, this article extends the application of the predictive coding framework to the visual arts. It does so by linking this theoretical discussion to a subjective, phenomenological account of how a work of art is experienced. In order to engage more deeply with a work of art, viewers must be able to tune or adapt their prediction mechanism to recognize art as a specific class of objects whose ontological nature defies predictability, and they must be able to sustain a productive flow of predictions from low-level sensory, recognitional to abstract semantic, conceptual, and affective inferences. The affective component of the process of predictive error optimization that occurs when a viewer enters into dialog with a painting is constituted both by activating the affective affordances within the image and by the affective consequences of prediction error minimization itself. The predictive coding framework also has implications for the problem of the culturality of vision. A person's mindset, which determines what top-down expectations and predictions are generated, is co-constituted by culture-relative skills and knowledge, which form hyperpriors that operate in the perception of art.

• Keywords
• affective affordance, art experience, art perception, culturality of vision, predictive coding, predictive error minimization, reward in art,
• Publication type
• Journal Article MeSH

We argue that prediction success maximization is a basic objective of cognition and cortex, that it is compatible with but distinct from prediction error minimization, that neither objective requires subtractive coding, that there is clear neurobiological evidence for the amplification of predicted signals, and that we are unconvinced by evidence proposed in support of subtractive coding. We outline recent discoveries showing that pyramidal cells on which our cognitive capabilities depend usually transmit information about input to their basal dendrites and amplify that transmission when input to their distal apical dendrites provides a context that agrees with the feedforward basal input in that both are depolarizing, i.e., both are excitatory rather than inhibitory. Though these intracellular discoveries require a level of technical expertise that is beyond the current abilities of most neuroscience labs, they are not controversial and acclaimed as groundbreaking. We note that this cellular cooperative context-sensitivity greatly enhances the cognitive capabilities of the mammalian neocortex, and that much remains to be discovered concerning its evolution, development, and pathology.

• Keywords
• Apical amplification, Coherent Infomax, perceptual inference, prediction error minimisation, predictive coding,
• Publication type
• Journal Article MeSH
• Review MeSH

Osteoarthritis (OA) is a frequent musculoskeletal disorder affecting millions of people worldwide. Despite advances in understanding the pathogenesis of OA, prognostic biomarkers or effective targeted treatment are not currently available. Research on epigenetic factors has yielded some new insights as new technologies for their detection continue to emerge. In this context, non-coding RNAs, including microRNAs, long non-coding RNAs, circular RNAs, piwi-interacting RNAs, and small nucleolar RNAs, regulate intracellular signaling pathways and biological processes that have a crucial role in the development of several diseases. In this review, we present current knowledge on the role of epigenetic factors with a focus on non-coding RNAs in the development, prediction and treatment of OA. This article is categorized under: RNA in Disease and Development > RNA in Disease.

• Keywords
• biomarker, epigenetic factors, non-coding RNA, osteoarthritis, targeted treatment,
• MeSH
• RNA, Circular MeSH
• Humans MeSH
• MicroRNAs * genetics   MeSH
• Osteoarthritis * genetics   MeSH
• Piwi-Interacting RNA MeSH
• RNA, Long Noncoding * genetics   MeSH
• Check Tag
• Humans MeSH
• Publication type
• Journal Article MeSH
• Research Support, Non-U.S. Gov't MeSH
• Review MeSH
• Names of Substances
• RNA, Circular MeSH
• MicroRNAs * MeSH
• Piwi-Interacting RNA MeSH
• RNA, Long Noncoding * MeSH

BACKGROUND: The first systematic study of small non-coding RNAs (sRNA, ncRNA) in Streptomyces is presented. Except for a few exceptions, the Streptomyces sRNAs, as well as the sRNAs in other genera of the Actinomyces group, have remained unstudied. This study was based on sequence conservation in intergenic regions of Streptomyces, localization of transcription termination factors, and genomic arrangement of genes flanking the predicted sRNAs. RESULTS: Thirty-two potential sRNAs in Streptomyces were predicted. Of these, expression of 20 was detected by microarrays and RT-PCR. The prediction was validated by a structure based computational approach. Two predicted sRNAs were found to be terminated by transcription termination factors different from the Rho-independent terminators. One predicted sRNA was identified computationally with high probability as a Streptomyces 6S RNA. Out of the 32 predicted sRNAs, 24 were found to be structurally dissimilar from known sRNAs. CONCLUSION: Streptomyces is the largest genus of Actinomyces, whose sRNAs have not been studied. The Actinomyces is a group of bacterial species with unique genomes and phenotypes. Therefore, in Actinomyces, new unique bacterial sRNAs may be identified. The sequence and structural dissimilarity of the predicted Streptomyces sRNAs demonstrated by this study serve as the first evidence of the uniqueness of Actinomyces sRNAs.

• MeSH
• Algorithms MeSH
• RNA, Bacterial chemistry   genetics   MeSH
• Species Specificity MeSH
• Genome, Bacterial MeSH
• DNA, Intergenic MeSH
• Nucleic Acid Conformation MeSH
• Models, Molecular MeSH
• RNA, Untranslated chemistry   genetics   MeSH
• Reverse Transcriptase Polymerase Chain Reaction MeSH
• Base Sequence MeSH
• Oligonucleotide Array Sequence Analysis MeSH
• Streptomyces coelicolor genetics   MeSH
• Streptomyces genetics   MeSH
• Terminator Regions, Genetic MeSH
• Computational Biology MeSH
• Publication type
• Journal Article MeSH
• Research Support, Non-U.S. Gov't MeSH
• Validation Study MeSH
• Names of Substances
• RNA, Bacterial MeSH
• DNA, Intergenic MeSH
• RNA, Untranslated MeSH

BACKGROUND: To provide an overview of the importance of long non-coding RNAs (lncRNAs) in the pathogenesis of renal cell carcinoma and their utility as bio-markers for dia-gnosis, prognosis and prediction of treatment response. MATERIALS AND METHODS: A literature search in the Pubmed and Web of Science databases using the keywords variations of “long non-coding RNA” (“lncRNA”, “long noncoding RNA”, “long non-coding RNA”) and “renal cell carcinoma” (“renal cancer”, “renal cell carcinoma”, “kidney cancer”) was performed. The results related to the pathogenesis, dia-gnosis, prognosis and use as therapeutic targets were separated. RESULTS: Long non-coding RNAs regulate gene expression at different levels. They act both as oncogenes and tumor suppressors. The mechanism of their action has not been fully elucidated, but they are actively involved in the regulation of hypoxia inducible factors pathway, epithelial-mesenchymal transition, cell proliferation, cell cycle regulation, apoptosis, local invasion and development of metastases. Aberrant expression in tumor tissue compared to healthy parenchyma and the correlation of expression levels with clinical-pathological features allow the potential use of many lncRNAs as bio-markers for early detection and prognosis of the disease, including the response to targeted therapy. In vitro assays indicate the potential use of lncRNAs as therapeutic targets. CONCLUSION: Our knowledge of long non-coding RNAs in relation to renal cell carcinoma is increasing rapidly. At present, some of them can be considered as promising bio-markers. Further research is needed before they can be introduced into routine clinical practice.

• Keywords
• bio­marker, dia­gnosis, long non-coding RNA, prognosis, renal cell carcinoma,
• MeSH
• Cell Cycle genetics   MeSH
• Epithelial-Mesenchymal Transition genetics   MeSH
• Carcinoma, Renal Cell diagnosis   genetics   mortality   therapy   MeSH
• Humans MeSH
• Biomarkers, Tumor genetics   MeSH
• Kidney Neoplasms diagnosis   genetics   mortality   therapy   MeSH
• Prognosis MeSH
• Cell Proliferation genetics   MeSH
• Gene Expression Regulation, Neoplastic MeSH
• RNA, Long Noncoding genetics   MeSH
• Check Tag
• Humans MeSH
• Publication type
• Journal Article MeSH
• Review MeSH
• Names of Substances
• Biomarkers, Tumor MeSH
• RNA, Long Noncoding MeSH

Gliomas are the most common malignancies of the central nervous system. Because of tumor localization and the biological behavior of tumor cells, gliomas are characterized by very poor prognosis. Despite significant efforts that have gone into glioma research in recent years, the therapeutic efficacy of available treatment options is still limited, and only a few clinically usable diagnostic biomarkers are available. More and more studies suggest non-coding RNAs to be promising diagnostic biomarkers and therapeutic targets in many cancers, including gliomas. One of the largest groups of these molecules is long non-coding RNAs (lncRNAs). LncRNAs show promising potential because of their unique tissue expression patterns and regulatory functions in cancer cells. Understanding the role of lncRNAs in gliomas may lead to discovery of the novel molecular mechanisms behind glioma biological features. It may also enable development of new solutions to overcome the greatest obstacles in therapy of glioma patients. In this review, we summarize the current knowledge about lncRNAs and their involvement in the molecular pathology of gliomas. A conclusion follows that these RNAs show great potential to serve as powerful diagnostic, prognostic, and predictive biomarkers as well as therapeutic targets.

• Keywords
• biomarker, diagnosis, glioblastoma, glioma, long non-coding RNA, molecular pathology, prognosis,
• MeSH
• Glioma genetics   pathology   MeSH
• Humans MeSH
• Pathology, Molecular MeSH
• Biomarkers, Tumor genetics   MeSH
• Prognosis MeSH
• RNA, Long Noncoding genetics   MeSH
• Animals MeSH
• Check Tag
• Humans MeSH
• Animals MeSH
• Publication type
• Journal Article MeSH
• Review MeSH
• Names of Substances
• Biomarkers, Tumor MeSH
• RNA, Long Noncoding MeSH