Probing for consciousness in machines.

This study explores the potential for artificial agents to develop core consciousness, as proposed by Antonio Damasio's theory of consciousness. According to Damasio, the emergence of core consciousness relies on the integration of a self model, informed by representations of emotions and feelings, and a world model. We hypothesize that an artificial agent, trained via reinforcement learning (RL) in a virtual environment, can develop preliminary forms of these models as a byproduct of its primary task.

A robust sampling technique for realistic distribution simulation in federated learning.

Purpose: Federated Learning helps training deep learning networks with diverse data from different locations, particularly in restricted clinical settings. However, label distributions overlapping only partially across clients, due to different demographics, may significantly harm the global training, and thus local model performance. Investigating such effects before rolling out large-scale Federated Learning setups requires proper sampling of the expected label distributions.

Drug Repurposing for Kala-Azar.

Visceral leishmaniasis (VL), a Neglected Tropical Disease caused by , remains insufficiently addressed by current therapies due to high toxicity, poor efficacy, and immunosuppressive complications. This study aimed to identify and characterize repurposed drugs that simultaneously target parasite-encoded and host-associated mechanisms essential for VL pathogenesis. Two complementary in silico drug repurposing strategies were employed.

Cryogenic 750-mJ Ti:sapphire amplifier for laser plasma acceleration at a 100-Hz repetition rate.

Laser plasma acceleration (LPA) is rapidly evolving from proof-of-principle experiments to stable and reliable accelerator operation. An important next step in this evolution is to increase the repetition rate to enable performance improvements through active stabilization of electron parameters using fast feedback loops. Today, laser-plasma accelerators are typically driven by Ti:sapphire lasers, but their high quantum defect and pump laser requirements limit the repetition rate and average power.

Quantum annealing feature selection on light-weight medical image datasets.

We investigate the use of quantum computing algorithms on real quantum hardware to tackle the computationally intensive task of feature selection for light-weight medical image datasets. Feature selection is often formulated as a k of n selection problem, where the complexity grows binomially with increasing k and n. Quantum computers, particularly quantum annealers, are well-suited for such problems, which may offer advantages under certain problem formulations.

Nonlinear Neural Dynamics and Classification Accuracy in Reservoir Computing.

Reservoir computing information processing based on untrained recurrent neural networks with random connections is expected to depend on the nonlinear properties of the neurons and the resulting oscillatory, chaotic, or fixed-point dynamics of the network. However, the degree of nonlinearity required and the range of suitable dynamical regimes for a given task remain poorly understood. To clarify these issues, we study the classification accuracy of a reservoir computer in artificial tasks of varying complexity while tuning both the neuron's degree of nonlinearity and the reservoir's dynamical regime.

Corrigendum: Advancement and independent validation of a deep learning-based tool for automated scoring of nail psoriasis severity using the modified nail psoriasis severity index.

[This corrects the article DOI: 10.3389/fmed.2025.

The archaeology of climate change: a blueprint for integrating environmental and cultural systems.

Cultural systems play an important role in shaping the interactions between humans and the environment, and are in turn shaped by these interactions. However, at present, cultural systems are poorly integrated into the models used by climate scientists to study the interaction of natural and anthropogenic processes (i.e.

High-Resolution, Motion-Corrected, Volume-Fused OCT for Investigating Longitudinal Changes in Subretinal Drusenoid Deposits in Intermediate AMD.

Purpose: To demonstrate high-resolution, motion-corrected, volume-fused optical coherence tomography (OCT) for assessing longitudinal changes in macular dot form subretinal drusenoid deposits (SDDs).

Methods: Six consecutive isotropic volume raster scans over 6 × 6 mm (500 × 500 A-scans) were acquired using a high-resolution (2.7 µm axial resolution) spectral domain OCT prototype instrument.

Bruch's Membrane Opening-Based Peripapillary Optical Coherence Tomography Angiography Analysis.

Introduction: Peripapillary optical coherence tomography angiography (OCT-A) scans are a challenge for vessel density (VD) analysis, being dependent on demarcation of the optic disc. Longitudinal VD analysis requires that each pixel of the OCT-A scan must be at the exact same location during follow-up scans in order to see inter-visit differences. The aim of the present study was to investigate reliability of Bruch's membrane opening (BMO)-based peripapillary OCT-A analysis with and without the implementation of the anatomical positioning system (APS) compared to manual analysis.

A self-supervised multimodal deep learning approach to differentiate post-radiotherapy progression from pseudoprogression in glioblastoma.

Accurate differentiation of pseudoprogression (PsP) from True Progression (TP) following radiotherapy (RT) in glioblastoma patients is crucial for optimal treatment planning. However, this task remains challenging due to the overlapping imaging characteristics of PsP and TP. This study therefore proposes a multimodal deep-learning approach utilizing complementary information from routine anatomical MR images, clinical parameters, and RT treatment planning information for improved predictive accuracy.

Nuremberg Letterbooks: A Multi-Transcriptional Dataset of Early 15th Century Manuscripts for Document Analysis.

Most datasets in the field of document analysis utilize highly standardized labels, which, while simplifying specific tasks, often produce outputs that are not directly applicable to humanities research. In contrast, the Nuremberg Letterbooks dataset, which comprises historical documents from the early 15th century, addresses this gap by providing multiple types of transcriptions and accompanying metadata. This approach allows for developing methods that are more closely aligned with the needs of the humanities.

Toward Noninvasive High-Resolution In Vivo pH Mapping in Brain Tumors by P-Informed deepCEST MRI.

The intracellular pH (pH) is critical for understanding various pathologies, including brain tumors. While conventional pH measurement through P-MRS suffers from low spatial resolution and long scan times, H-based APT-CEST imaging offers higher resolution with shorter scan times. This study aims to directly predict P-pH maps from CEST data by using a fully connected neuronal network.

Deep learning-based classification of coronary arteries and left ventricle using multimodal data for autonomous protocol selection or adjustment in angiography.

Optimal selection of X-ray imaging parameters is crucial in coronary angiography and structural cardiac procedures to ensure optimal image quality and minimize radiation exposure. These anatomydependent parameters are organized into customizable organ programs, but manual selection of the programs increases workload and complexity. Our research introduces a deep learning algorithm that autonomously detects three target anatomies:the left coronary artery (LCA), right coronary artery (RCA), and left ventricle (LV),based on singleX-ray frames without vessel structure and enables adjustment of imaging parameters by choosing the appropriate organ program.

Advancement and independent validation of a deep learning-based tool for automated scoring of nail psoriasis severity using the modified nail psoriasis severity index.

Objective: To improve and validate a convolutional neural network (CNN)-based model for the automated scoring of nail psoriasis severity using the modified Nail Psoriasis Severity Index (mNAPSI) with adequate accuracy across all severity classes and without dependency on standardized conditions.

Methods: Patients with psoriasis (PsO), psoriatic arthritis (PsA), and non-psoriatic controls including healthy individuals and patients with rheumatoid arthritis were included for training, while validation utilized an independent cohort of psoriatic patients. Nail photographs were pre-processed and segmented and mNAPSI scores were annotated by five expert readers.

Synthetic electroretinogram signal generation using a conditional generative adversarial network.

Purpose: The electroretinogram (ERG) records the functional response of the retina. In some neurological conditions, the ERG waveform may be altered and could support biomarker discovery. In heterogeneous or rare populations, where either large data sets or the availability of data may be a challenge, synthetic signals with Artificial Intelligence (AI) may help to mitigate against these factors to support classification models.

Fully automatic HER2 tissue segmentation for interpretable HER2 scoring.

Breast cancer is the most common cancer in women, with HER2 (human epidermal growth factor receptor 2) overexpression playing a critical role in regulating cell growth and division. HER2 status, assessed according to established scoring guidelines, offers important information for treatment selection. However, the complexity of the task leads to variability in human rater assessments.

Hunter-gatherer sea voyages extended to remotest Mediterranean islands.

The Maltese archipelago is a small island chain that is among the most remote in the Mediterranean. Humans were not thought to have reached and inhabited such small and isolated islands until the regional shift to Neolithic lifeways, around 7.5 thousand years ago (ka).

Parametric-MAA: fast, object-centric avoidance of metal artifacts for intraoperative CBCT.

Purpose: Metal artifacts remain a persistent issue in intraoperative CBCT imaging. Particularly in orthopedic and trauma applications, these artifacts obstruct clinically relevant areas around the implant, reducing the modality's clinical value. Metal artifact avoidance (MAA) methods have shown potential to improve image quality through trajectory adjustments, but often fail in clinical practice due to their focus on irrelevant objects and high computational demands.

Large scale and regional demographic responses to climatic changes in Europe during the Final Palaeolithic.

The European Final Palaeolithic witnessed marked changes in almost all societal domains. Despite a rich body of evidence, our knowledge of human palaeodemographic processes and regional population dynamics still needs to be improved. In this study, we present regionally differentiated population estimates for the Greenland Interstadial 1d-a (GI-1d-a; 14-12.

Improved MRI detection of inflammation-induced changes in bone marrow microstructure in mice: a machine learning-enhanced T2 distribution analysis.

Background: We investigated inflammation-induced changes in femoral hematopoietic bone marrow using advanced magnetic resonance imaging (MRI) techniques, including T2-weighted imaging, scalar T2 mapping, and machine learning-enhanced T2 distribution analysis to improve the detection of bone marrow microstructural alterations. Findings were correlated with histological markers and systemic inflammation.

Methods: Using a 9.

Deep learning-based image analysis in muscle histopathology using photo-realistic synthetic data.

Background: Artificial intelligence (AI), specifically Deep learning (DL), has revolutionized biomedical image analysis, but its efficacy is limited by the need for representative, high-quality large datasets with manual annotations. While latest research on synthetic data using AI-based generative models has shown promising results to tackle this problem, several challenges such as lack of interpretability and need for vast amounts of real data remain. This study aims to introduce a new approach-SYNTA-for the generation of photo-realistic synthetic biomedical image data to address the challenges associated with state-of-the art generative models and DL-based image analysis.

DRACO: differentiable reconstruction for arbitrary CBCT orbits.

. This study introduces a novel method for reconstructing cone beam computed tomography (CBCT) images for arbitrary orbits, addressing the computational and memory challenges associated with traditional iterative reconstruction algorithms..

Deep learning-based debris flow hazard detection and recognition system: a case study.

Debris flows are characterized by their suddenness, rapidity, large scale and destructive power, causing serious threat to the population in mountainous areas. Surveillance cameras are widely used in geological hazard monitoring and early warning projects. So far, video cameras are used as a passive tool for post inspection and not as an active role for debris flow monitoring and early warning.

A validated Monte Carlo model for a CdTe-based photon-counting detector at higher flux rates.

Statistical properties of a CdTe photon-counting detector were simulated using a dedicated Monte Carlo model that includes spatial and spectral correlations. A measurement of the same properties was done to validate the simulation and gain further understanding of the detector.Photon histories were calculated using a Monte Carlo x-ray simulation program using energy dependent interaction probabilities of the incoming photons.