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

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

OBJECTIVES: Compare overall Landing Error Scoring System (LESS) scores, risk categorisation, specific LESS errors, and double-leg jump-landing jump heights between overhead goal and no goal conditions. DESIGN: Randomised cross-over. SETTING: Laboratory. PARTICIPANTS: 76 (51% male). MAIN OUTCOME MEASURES: Participants landed from a 30-cm box to 50% of their body height and immediately jumped vertically for maximum height. Participants completed three trials under two random-ordered conditions: with and without overhead goal. Group-level mean LESS scores, risk categorisation (5-error threshold), specific landing errors, and jump heights were compared between conditions. RESULTS: Mean LESS scores were greater (0.3 errors, p < 0.001) with the overhead goal, but this small difference was not clinically meaningful. Similarly, although the number of high-risk participants was greater with the overhead goal (p = 0.039), the 9.2% difference was trivial. Participants jumped 2.7 cm higher with the overhead goal (p < 0.001) without affecting the occurrence of any specific LESS errors. DISCUSSION: Performing the LESS with an overhead goal enhances sport specificity and elicits greater vertical jump performances with minimal change in landing errors and injury-risk categorisation. Adding an overhead goal to LESS might enhance its suitability for injury risk screening, although the predictive value of LESS with an overhead goal needs confirmation.

• Keywords
• Anterior cruciate ligament, Injury risk, Jump-landing biomechanics, Movement screen,
• MeSH
• Cross-Over Studies MeSH
• Knee Joint MeSH
• Humans MeSH
• Movement MeSH
• Anterior Cruciate Ligament Injuries * MeSH
• Sports * MeSH
• Check Tag
• Humans MeSH
• Male MeSH
• Female MeSH
• Publication type
• Journal Article MeSH
• Randomized Controlled Trial MeSH

During the minimally-invasive liver surgery, only the partial surface view of the liver is usually provided to the surgeon via the laparoscopic camera. Therefore, it is necessary to estimate the actual position of the internal structures such as tumors and vessels from the pre-operative images. Nevertheless, such task can be highly challenging since during the intervention, the abdominal organs undergo important deformations due to the pneumoperitoneum, respiratory and cardiac motion and the interaction with the surgical tools. Therefore, a reliable automatic system for intra-operative guidance requires fast and reliable registration of the pre- and intra-operative data. In this paper we present a complete pipeline for the registration of pre-operative patient-specific image data to the sparse and incomplete intra-operative data. While the intra-operative data is represented by a point cloud extracted from the stereo-endoscopic images, the pre-operative data is used to reconstruct a biomechanical model which is necessary for accurate estimation of the position of the internal structures, considering the actual deformations. This model takes into account the patient-specific liver anatomy composed of parenchyma, vascularization and capsule, and is enriched with anatomical boundary conditions transferred from an atlas. The registration process employs the iterative closest point technique together with a penalty-based method. We perform a quantitative assessment based on the evaluation of the target registration error on synthetic data as well as a qualitative assessment on real patient data. We demonstrate that the proposed registration method provides good results in terms of both accuracy and robustness w.r.t. the quality of the intra-operative data.

• Keywords
• Minimally-invasive surgery, Non-rigid registration, Patient-specific modeling, Real-time simulation,
• MeSH
• Models, Biological * MeSH
• Precision Medicine methods   MeSH
• Liver surgery   MeSH
• Humans MeSH
• Minimally Invasive Surgical Procedures methods   MeSH
• Check Tag
• Humans MeSH
• Male MeSH
• Female MeSH
• Publication type
• Journal Article MeSH

Accurate prediction of NMR parameters from first principles is essential for the structural characterization of molecular solids. Recent studies have shown that single-molecule correction schemes-based on hybrid DFT calculations-can significantly improve the accuracy of periodic DFT predictions of nuclear shieldings. Here, we evaluate the performance of this correction approach not only for periodic DFT calculations but also for ShiftML2, a machine-learning model trained on PBE-calculated NMR data. For 13C nuclei, the application of single-molecule PBE0 corrections to periodic PBE shieldings has reduced the root-mean-square deviation (RMSD) from 2.18 to 1.20 ppm, with negligible improvement observed for 1H. When applied to ShiftML2 predictions, the corrections have yielded a smaller reduction in 13C RMSD (from 3.02 to 2.51 ppm); again, they have had minimal impact on 1H predictions. Residual analysis has revealed weak correlation between DFT and ML errors, suggesting that while some sources of systematic deviation may be shared, others are likely distinct. These results demonstrate that DFT-specific correction schemes do not straightforwardly translate to machine-learning models, highlighting the need for ML-tailored post-processing or retraining strategies. The findings have important implications for the integration of machine learning into high-throughput NMR workflows and the development of more accurate predictive tools for solid-state spectroscopy.

• Publication type
• Journal Article MeSH

G-quadruplexes (G4s), a type of non-B DNA, play important roles in a wide range of molecular processes, including replication, transcription, and translation. Genome integrity relies on efficient and accurate DNA synthesis, and is compromised by various stressors, to which non-B DNA structures such as G4s can be particularly vulnerable. However, the impact of G4 structures on DNA polymerase fidelity is largely unknown. Using an in vitro forward mutation assay, we investigated the fidelity of human DNA polymerases delta (δ4, four-subunit), eta (η), and kappa (κ) during synthesis of G4 motifs representing those in the human genome. The motifs differ in sequence, topology, and stability, features that may affect DNA polymerase errors. Polymerase error rate hierarchy (δ4 < κ < η) is largely maintained during G4 synthesis. Importantly, we observed unique polymerase error signatures during synthesis of VEGF G4 motifs, stable G4s which form parallel topologies. These statistically significant errors occurred within, immediately flanking, and encompassing the G4 motif. For pol δ4, the errors were deletions, insertions and complex errors within the G4 or encompassing the G4 motif and surrounding sequence. For pol η, the errors occurred in 3' sequences flanking the G4 motif. For pol κ, the errors were frameshift mutations within G-tracts of the G4. Because these error signatures were not observed during synthesis of an antiparallel G4 and, to a lesser extent, a hybrid G4, we suggest that G4 topology and/or stability could influence polymerase fidelity. Using in silico analyses, we show that most polymerase errors are predicted to have minimal effects on predicted G4 stability. Our results provide a unique view of G4s not previously elucidated, showing that G4 motif heterogeneity differentially influences polymerase fidelity within the motif and flanking sequences. Thus, our study advances the understanding of how DNA polymerase errors contribute to G4 mutagenesis.

• Keywords
• DNA polymerase δ, DNA polymerase η, DNA polymerase κ, DNA replication, Error signature, Non-B DNA,
• MeSH
• DNA-Directed DNA Polymerase genetics   metabolism   MeSH
• DNA genetics   MeSH
• G-Quadruplexes * MeSH
• Humans MeSH
• DNA Replication MeSH
• Vascular Endothelial Growth Factor A genetics   MeSH
• Check Tag
• Humans MeSH
• Publication type
• Journal Article MeSH
• Research Support, Non-U.S. Gov't MeSH
• Research Support, N.I.H., Extramural MeSH
• Names of Substances
• DNA-Directed DNA Polymerase MeSH
• DNA MeSH
• Vascular Endothelial Growth Factor A MeSH

Organic photovoltaic (OPV) cells are at the forefront of sustainable energy generation due to their lightness, flexibility, and low production costs. These characteristics make OPVs a promising solution for achieving sustainable development goals. However, predicting their lifetime remains challenging task due to complex interactions between internal factors such as material degradation, interface stability, and morphological changes, and external factors like environmental conditions, mechanical stress, and encapsulation quality. In this study, we propose a machine learning-based technique to predict the degradation over time of OPVs. Specifically, we employ multi-layer perceptron (MLP) and long short-term memory (LSTM) neural networks to predict the power conversion efficiency (PCE) of inverted organic solar cells (iOSCs) made from the blend PTB7-Th:PC70BM, with PFN as the electron transport layer (ETL), fabricated under an N2 environment. We evaluate the performance of the proposed technique using several statistical metrics, including mean squared error (MSE), root mean squared error (rMSE), relative squared error (RSE), relative absolute error (RAE), and the correlation coefficient (R). The results demonstrate the high accuracy of our proposed technique, evidenced by the minimal error between predicted and experimentally measured PCE values: 0.0325 for RSE, 0.0729 for RAE, 0.2223 for rMSE, and 0.0541 for MSE using the LSTM model. These findings highlight the potential of proposed models in accurately predicting the performance of OPVs, thus contributing to the advancement of sustainable energy technologies.

• Keywords
• Degradation, Inverted organic solar cells, Long short-term memory, Machine learning, Multi-layer perceptron, Power conversion efficiency, Prediction,
• Publication type
• Journal Article MeSH

Potentially toxic elements in agricultural soils are primarily derived from anthropogenic and geogenic sources. This study aims to predict and map antimony (Sb) concentration in soil using multiple regression kriging in two distinct modeling approaches, namely Sb prediction using data fusion coupled with regression kriging (scenario 1) and Sb prediction using data fusion, terrain attributes, and regression kriging (scenario 2). Cubist regression kriging (cubist_RK), conditional inference forest regression kriging (CIF_RK), extreme gradient boosting regression kriging (EGB_RK) and random forest regression kriging (RF_RK) were the modeling techniques used in the estimation of Sb concentration in agricultural soil. The validation results suggested that in scenario 1, EGB_RK was the optimal modeling approach for Sb prediction in agricultural soil with root mean square error (RMSE) = 1.31 and mean absolute error (MAE) = 0.61, bias = 0.37, and high coefficient of determination R2 = 0.81. Similarly, the EGB_RK was also the optimal modeling approach in scenario 2, with the highest R2 = 0.76, RMSE = 0.90, bias = 0.06, and MAE = 0.48 values than the other regression kriging modeling approaches. The cumulative assessment suggested that the EGB_RK in scenario 2 yielded optimal results compared to the respective modeling approach in scenario 1. The uncertainty propagated by the modeling approaches in both scenarios indicated that the degree of uncertainty during the modeling process was distributed across the study area from a low to a moderate uncertainty level. However, cubist_RK in scenario 2 exhibited some elevated spots of uncertainty levels. As a result, the combination of data fusion, terrain attributes, and regression kriging modeling approaches produces optimal results with a high R2 value, minimal errors as well as bias. Furthermore, combining terrain attributes with data fusion is promising for reducing model error, bias and yielding high-accuracy predictions.

• Keywords
• Agricultural soil, Data fusion, Regression kriging, Terrain attributes, Uncertainty,
• MeSH
• Antimony * MeSH
• Spatial Analysis MeSH
• Soil * MeSH
• Agriculture MeSH
• Publication type
• Journal Article MeSH
• Names of Substances
• Antimony * MeSH
• Soil * MeSH

This paper proposes development of optimized heterogeneous ensemble models for prediction of responses based on given sets of input parameters for wire electrical discharge machining (WEDM) processes, which have found immense applications in many of the present-day manufacturing industries because of their ability to generate complicated 2D and 3D profiles on hard-to-machine engineering materials. These ensembles are developed combining predictions of the three base models, i.e. random forest, support vector machine and ridge regression. These three base models are first framed utilizing the training datasets, providing predictions for all the responses under consideration. Based on these predictions, two optimization problems are formulated for each of the responses, while minimizing root mean squared error and mean absolute error, for subsequent development of two optimized ensembles whose predictions are the weighted sum of the predictions of the base models. The prediction performance of all the five models is ascertained through nine statistical metrics, after which a cumulative quality loss-based multi-response signal-to-noise (MRSN) ratio for each model is computed, for each of the responses, where a higher MRSN ratio indicates greater accuracy in prediction. This study is conducted using two experimental datasets of WEDM process. Overall, the optimized ensemble models having higher MRSN ratios than the base models are indicated to deliver better prediction accuracy.

• Keywords
• Multi-response S/N ratio, Optimized heterogeneous ensemble, Prediction performance, Response, Wire electrical discharge machining,
• Publication type
• Journal Article MeSH